#587 by GitHub stars
mims-harvard/ToolUniverse
Democratizing AI scientists with ToolUniverse
History
Review weekly changes to SKILL.md before updating. Dates mark the weeks in which we observed each version; checks apply to the recorded commits.
View weekly changes
plugin/skills/setup-tooluniverse/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-acmg-variant-classification/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-admet-prediction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-adverse-event-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-adverse-outcome-pathway/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-aging-senescence/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-antibody-engineering/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-antigravity-plugin/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-binder-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-bioinformatics-resource-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-biomedical-fact-lookup/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-cancer-classification/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-cancer-genomics-tcga/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-cancer-variant-interpretation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-cell-line-profiling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-chemical-compound-retrieval/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-chemical-safety/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-chemical-sourcing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-claude-code-plugin/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-clinical-data-integration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-clinical-guidelines/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-clinical-risk-scoring/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-clinical-terminology-lookup/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-clinical-trial-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-clinical-trial-matching/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-clinical-trial-protocol/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-comparative-genomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-computational-biophysics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-connect/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-crispr-screen-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-custom-tool/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-data-integration-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-data-wrangling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-database-logins/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-dataset-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-diagnostic-test-evaluation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-disease-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-dose-response/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-drug-drug-interaction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-drug-mechanism-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-drug-regulatory/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-drug-repurposing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-drug-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-drug-synergy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-drug-target-validation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-ecology-biodiversity/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-electron-microscopy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-enzyme-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-enzyme-kinetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-epidemiological-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-epigenomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-epigenomics-chromatin/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-expression-data-retrieval/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-fastq-qc/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-functional-genomics-screens/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gene-disease-association/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gene-enrichment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gene-liability/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gene-regulatory-networks/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-glycobiology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gpcr-structural-pharmacology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gwas-drug-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gwas-finemapping/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gwas-snp-interpretation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gwas-study-explorer/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-gwas-trait-to-gene/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-hla-immunogenomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-image-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-immune-repertoire-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-immunology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-immunotherapy-response-prediction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-infectious-disease/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-inorganic-physical-chemistry/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-install-skills/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-kegg-disease-drug/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-lab-data-standardization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-lab-protocols/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-lipidomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-literature-deep-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-medical-imaging-radiology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-mendelian-randomization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-meta-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-metabolomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-metabolomics-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-metabolomics-pathway/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-metagenomics-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-microbial-genome-characterization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-microbiome-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-ml-inference-utility/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-model-organism-genetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-molecular-cloning/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-multi-omics-integration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-multiomic-disease-characterization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-natural-product-dereplication/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-network-pharmacology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-neuroscience/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-neurosynth-meta-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-nfcore-pipelines/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-nih-funding-landscape/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-noncoding-rna/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-nutrition-food-composition/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-organic-chemistry/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-pathway-disease-genetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-peptide-target-deorphanization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-pharmacogenomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-pharmacokinetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-pharmacovigilance/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-phewas/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-phylogenetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-plant-genomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-polygenic-risk-score/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-population-genetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-population-genetics-1000genomes/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-precision-medicine-stratification/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-precision-oncology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-primer-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-product-safety-surveillance/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-protein-design-campaign/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-protein-interactions/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-protein-lof-mechanism/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-protein-modification-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-protein-sae-variant-interpretation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-protein-structural-annotation-pdb/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-protein-structure-prediction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-protein-structure-retrieval/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-protein-therapeutic-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-proteomics-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-proteomics-data-retrieval/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-publish-tool/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-rare-disease-diagnosis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-rare-disease-genomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-regulatory-genomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-regulatory-variant-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-residue-functional-mechanism-interpretation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-rnaseq-deseq2/SKILL.md
Week of Nov 2, 2026 · +13 / −13 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
- 33 # Single-factor sex DE on a CD4/CD8 subset, with FAM138A LFC
+ 33 # Single-factor DE on a cell-type subset, reporting one gene's LFC
- 38 --subset-col celltype --subset-values "CD4,CD8" \
+ 38 --subset-col celltype --subset-values "<type-1>,<type-2>" \
- 40 --report-genes FAM138A \
+ 40 --report-genes "<GENE_SYMBOL>" \
- 46 # CONTRAST sex_M_vs_F: n=37496 n_tested=26591
+ 46 # CONTRAST <contrast>: n=<genes-in> n_tested=<genes-after-filtering>
- 49 # GENE FAM138A [sex_M_vs_F]: baseMean=... unshrunkLFC=... shrunkLFC=... padj=...
+ 49 # GENE <GENE_SYMBOL> [<contrast>]: baseMean=... unshrunkLFC=... shrunkLFC=... padj=...
- 57 --design "~Replicate + Strain + Media" \
- 58 --multi-contrast "Strain,97,1;Strain,98,1;Strain,99,1" \
- 59 --exclude-samples "resub-5,resub-10,resub-33" \
+ 57 --design "~replicate + group + media" \
+ 58 --multi-contrast "group,g1,ctrl;group,g2,ctrl;group,g3,ctrl" \
+ 59 --exclude-samples "sample-7,sample-12" \
- 127 --deg-csv "JBX97=/tmp/strain_venn/res_unshrunk_Strain_97_vs_1.csv" \
- 128 --deg-csv "JBX98=/tmp/strain_venn/res_unshrunk_Strain_98_vs_1.csv" \
- 129 --deg-csv "JBX99=/tmp/strain_venn/res_unshrunk_Strain_99_vs_1.csv" \
+ 127 --deg-csv "g1=/tmp/venn_run/res_unshrunk_group_g1_vs_ctrl.csv" \
+ 128 --deg-csv "g2=/tmp/venn_run/res_unshrunk_group_g2_vs_ctrl.csv" \
+ 129 --deg-csv "g3=/tmp/venn_run/res_unshrunk_group_g3_vs_ctrl.csv" \
- 131 --target-set "JBX97,JBX99"
+ 131 --target-set "g1,g3"
- 256 - **"NOT in any other strain/condition" — enumerate ALL strains/conditions in metadata**: When a question asks "% of genes DE in A AND DE in B AND **NOT in any other strain**", "other" refers to the FULL set of strains in the metadata file — not just the obvious counterpart. List all unique values in the relevant metadata column (e.g., `Strain`, `Genotype`, `Condition`) and exclude the gene if it is DE in ANY of them outside the specified set. Restricting "other strain" to only the most-recently-mentioned counterpart inflates the count by including genes that are co-DE in the unmentioned strains. Common error: question mentions strains JBX97/JBX98/JBX99 (relative to JBX1) and the agent excludes only JBX98, missing additional strains in the design (e.g., a fourth strain or media condition with its own DE set).
+ 256 - **"NOT in any other strain/condition" — enumerate ALL strains/conditions in metadata**: When a question asks "% of genes DE in A AND DE in B AND **NOT in any other strain**", "other" refers to the FULL set of strains in the metadata file — not just the obvious counterpart. List all unique values in the relevant metadata column (e.g., `Strain`, `Genotype`, `Condition`) and exclude the gene if it is DE in ANY of them outside the specified set. Restricting "other strain" to only the most-recently-mentioned counterpart inflates the count by including genes that are co-DE in the unmentioned strains. Common error: a question names two or three groups and the agent treats "other" as only the one named alongside them, missing the remaining levels in the design (a further group, or a second factor such as media with its own DE set).
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-scientific-peer-review/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-scientific-problem-selection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-scvi-tools/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-sdk/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-self-review/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-sequence-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-sequence-retrieval/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-share-computer/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-single-cell/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-small-molecule-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-spatial-omics-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-spatial-transcriptomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-statistical-modeling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-stem-cell-organoid/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-structural-proteomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-structural-variant-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-synthetic-biology-parts/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-systems-biology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-target-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-toxicology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-use-shared-computer/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-vaccine-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-variant-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-variant-functional-annotation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-variant-interpretation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-variant-predictor-dms-validation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugin/skills/tooluniverse-variant-to-mechanism/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/setup-tooluniverse/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-acmg-variant-classification/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-admet-prediction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-adverse-event-detection/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-adverse-outcome-pathway/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-aging-senescence/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-antibody-engineering/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-antigravity-plugin/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-binder-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-bioinformatics-resource-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-biomedical-fact-lookup/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-cancer-classification/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-cancer-genomics-tcga/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-cancer-variant-interpretation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-cell-line-profiling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-chemical-compound-retrieval/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-chemical-safety/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-chemical-sourcing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-clinical-data-integration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-clinical-guidelines/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-clinical-risk-scoring/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-clinical-terminology-lookup/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-clinical-trial-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-clinical-trial-matching/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-clinical-trial-protocol/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-codex-plugin/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-comparative-genomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-connect/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-crispr-screen-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-custom-tool/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-data-integration-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-data-wrangling/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-database-logins/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-dataset-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-diagnostic-test-evaluation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-disease-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-dose-response/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-drug-drug-interaction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-drug-mechanism-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-drug-regulatory/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-drug-repurposing/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-drug-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-drug-synergy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-drug-target-validation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-ecology-biodiversity/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-electron-microscopy/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-enzyme-design/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-enzyme-kinetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-epidemiological-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-epigenomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-epigenomics-chromatin/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-expression-data-retrieval/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-fastq-qc/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-functional-genomics-screens/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gene-disease-association/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gene-enrichment/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gene-liability/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gene-regulatory-networks/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-glycobiology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gpcr-structural-pharmacology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gwas-drug-discovery/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gwas-finemapping/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gwas-snp-interpretation/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gwas-study-explorer/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-gwas-trait-to-gene/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-hla-immunogenomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-image-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-immune-repertoire-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-immunology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-immunotherapy-response-prediction/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-infectious-disease/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-inorganic-physical-chemistry/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-install-skills/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-kegg-disease-drug/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-lab-data-standardization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-lab-protocols/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-lipidomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-literature-deep-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-medical-imaging-radiology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-mendelian-randomization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-meta-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-metabolomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-metabolomics-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-metabolomics-pathway/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-metagenomics-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-microbial-genome-characterization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-microbiome-research/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-ml-inference-utility/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-model-organism-genetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-molecular-cloning/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-multi-omics-integration/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-multiomic-disease-characterization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-natural-product-dereplication/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-network-pharmacology/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-neuroscience/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-neurosynth-meta-analysis/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-nih-funding-landscape/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-noncoding-rna/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-nutrition-food-composition/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-organic-chemistry/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-pathway-disease-genetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-peptide-target-deorphanization/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-pharmacogenomics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-pharmacokinetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-pharmacovigilance/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-phewas/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
169d065deeb3 → 5598a9fc97e3
Week of Oct 26, 2026 · Line counts unavailable
Before Not checked yet → After Network or broad shell
First observation → 169d065deeb3
No earlier version was observed for comparison.
plugins/tooluniverse/skills/tooluniverse-phylogenetics/SKILL.md
Week of Nov 2, 2026 · +0 / −0 lines
Before Network or broad shell → After Network or broad shell
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plugins/tooluniverse/skills/tooluniverse-plant-genomics/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-polygenic-risk-score/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-population-genetics/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-population-genetics-1000genomes/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-precision-medicine-stratification/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-precision-oncology/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-primer-design/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-product-safety-surveillance/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-protein-design-campaign/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-protein-interactions/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-protein-lof-mechanism/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-protein-modification-analysis/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-protein-sae-variant-interpretation/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-protein-structural-annotation-pdb/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-protein-structure-prediction/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-protein-structure-retrieval/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-protein-therapeutic-design/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-proteomics-analysis/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-proteomics-data-retrieval/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-publish-tool/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-rare-disease-diagnosis/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-rare-disease-genomics/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-regulatory-genomics/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-regulatory-variant-analysis/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-residue-functional-mechanism-interpretation/SKILL.md
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plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/SKILL.md
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- 33 # Single-factor sex DE on a CD4/CD8 subset, with FAM138A LFC
+ 33 # Single-factor DE on a cell-type subset, reporting one gene's LFC
- 38 --subset-col celltype --subset-values "CD4,CD8" \
+ 38 --subset-col celltype --subset-values "<type-1>,<type-2>" \
- 40 --report-genes FAM138A \
+ 40 --report-genes "<GENE_SYMBOL>" \
- 46 # CONTRAST sex_M_vs_F: n=37496 n_tested=26591
+ 46 # CONTRAST <contrast>: n=<genes-in> n_tested=<genes-after-filtering>
- 49 # GENE FAM138A [sex_M_vs_F]: baseMean=... unshrunkLFC=... shrunkLFC=... padj=...
+ 49 # GENE <GENE_SYMBOL> [<contrast>]: baseMean=... unshrunkLFC=... shrunkLFC=... padj=...
- 57 --design "~Replicate + Strain + Media" \
- 58 --multi-contrast "Strain,97,1;Strain,98,1;Strain,99,1" \
- 59 --exclude-samples "resub-5,resub-10,resub-33" \
+ 57 --design "~replicate + group + media" \
+ 58 --multi-contrast "group,g1,ctrl;group,g2,ctrl;group,g3,ctrl" \
+ 59 --exclude-samples "sample-7,sample-12" \
- 127 --deg-csv "JBX97=/tmp/strain_venn/res_unshrunk_Strain_97_vs_1.csv" \
- 128 --deg-csv "JBX98=/tmp/strain_venn/res_unshrunk_Strain_98_vs_1.csv" \
- 129 --deg-csv "JBX99=/tmp/strain_venn/res_unshrunk_Strain_99_vs_1.csv" \
+ 127 --deg-csv "g1=/tmp/venn_run/res_unshrunk_group_g1_vs_ctrl.csv" \
+ 128 --deg-csv "g2=/tmp/venn_run/res_unshrunk_group_g2_vs_ctrl.csv" \
+ 129 --deg-csv "g3=/tmp/venn_run/res_unshrunk_group_g3_vs_ctrl.csv" \
- 131 --target-set "JBX97,JBX99"
+ 131 --target-set "g1,g3"
- 256 - **"NOT in any other strain/condition" — enumerate ALL strains/conditions in metadata**: When a question asks "% of genes DE in A AND DE in B AND **NOT in any other strain**", "other" refers to the FULL set of strains in the metadata file — not just the obvious counterpart. List all unique values in the relevant metadata column (e.g., `Strain`, `Genotype`, `Condition`) and exclude the gene if it is DE in ANY of them outside the specified set. Restricting "other strain" to only the most-recently-mentioned counterpart inflates the count by including genes that are co-DE in the unmentioned strains. Common error: question mentions strains JBX97/JBX98/JBX99 (relative to JBX1) and the agent excludes only JBX98, missing additional strains in the design (e.g., a fourth strain or media condition with its own DE set).
+ 256 - **"NOT in any other strain/condition" — enumerate ALL strains/conditions in metadata**: When a question asks "% of genes DE in A AND DE in B AND **NOT in any other strain**", "other" refers to the FULL set of strains in the metadata file — not just the obvious counterpart. List all unique values in the relevant metadata column (e.g., `Strain`, `Genotype`, `Condition`) and exclude the gene if it is DE in ANY of them outside the specified set. Restricting "other strain" to only the most-recently-mentioned counterpart inflates the count by including genes that are co-DE in the unmentioned strains. Common error: a question names two or three groups and the agent treats "other" as only the one named alongside them, missing the remaining levels in the design (a further group, or a second factor such as media with its own DE set).
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plugins/tooluniverse/skills/tooluniverse-scientific-peer-review/SKILL.md
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Verified commit 5598a9fc97e32964fe9b703818e7d0747142f179
git clone --filter=blob:none --no-checkout https://github.com/mims-harvard/ToolUniverse.git .hotskills/mims-harvard-tooluniverse && git -C .hotskills/mims-harvard-tooluniverse checkout --detach 5598a9fc97e32964fe9b703818e7d0747142f179
# Copy each selected skill directory from .hotskills/mims-harvard-tooluniverse into .claude/skills/<skill-name>/Heat
4.1- Star growth, last 7 daysAhead of 67% of tracked repositories
- Star growth, last 24 hoursAhead of 1% of tracked repositories
- Rank movement, last 7 daysAhead of 21% of tracked repositories
- Commits, last 30 daysAhead of 95% of tracked repositories
- Accounts asking about it through MCP, last 7 daysNot available
- Hot votes this weekAhead of 0% of tracked repositories
Heat never changes the order by stars, and sponsorship never touches it. How heat works
Stars over time
Individual skills in this repository
- setup-tooluniverse
plugin/skills/setup-tooluniverse - tooluniverse
plugin/skills/tooluniverse - tooluniverse-acmg-variant-classification
plugin/skills/tooluniverse-acmg-variant-classification - tooluniverse-admet-prediction
plugin/skills/tooluniverse-admet-prediction - tooluniverse-adverse-event-detection
plugin/skills/tooluniverse-adverse-event-detection - tooluniverse-adverse-outcome-pathway
plugin/skills/tooluniverse-adverse-outcome-pathway - tooluniverse-aging-senescence
plugin/skills/tooluniverse-aging-senescence - tooluniverse-antibody-engineering
plugin/skills/tooluniverse-antibody-engineering - tooluniverse-antigravity-plugin
plugin/skills/tooluniverse-antigravity-plugin - tooluniverse-binder-discovery
plugin/skills/tooluniverse-binder-discovery - tooluniverse-bioinformatics-resource-discovery
plugin/skills/tooluniverse-bioinformatics-resource-discovery - tooluniverse-biomedical-fact-lookup
plugin/skills/tooluniverse-biomedical-fact-lookup - tooluniverse-cancer-classification
plugin/skills/tooluniverse-cancer-classification - tooluniverse-cancer-genomics-tcga
plugin/skills/tooluniverse-cancer-genomics-tcga - tooluniverse-cancer-variant-interpretation
plugin/skills/tooluniverse-cancer-variant-interpretation - tooluniverse-cell-line-profiling
plugin/skills/tooluniverse-cell-line-profiling - tooluniverse-chemical-compound-retrieval
plugin/skills/tooluniverse-chemical-compound-retrieval - tooluniverse-chemical-safety
plugin/skills/tooluniverse-chemical-safety - tooluniverse-chemical-sourcing
plugin/skills/tooluniverse-chemical-sourcing - tooluniverse-claude-code-plugin
plugin/skills/tooluniverse-claude-code-plugin - tooluniverse-clinical-data-integration
plugin/skills/tooluniverse-clinical-data-integration - tooluniverse-clinical-guidelines
plugin/skills/tooluniverse-clinical-guidelines - tooluniverse-clinical-risk-scoring
plugin/skills/tooluniverse-clinical-risk-scoring - tooluniverse-clinical-terminology-lookup
plugin/skills/tooluniverse-clinical-terminology-lookup - tooluniverse-clinical-trial-design
plugin/skills/tooluniverse-clinical-trial-design - tooluniverse-clinical-trial-matching
plugin/skills/tooluniverse-clinical-trial-matching - tooluniverse-clinical-trial-protocol
plugin/skills/tooluniverse-clinical-trial-protocol - tooluniverse-comparative-genomics
plugin/skills/tooluniverse-comparative-genomics - tooluniverse-computational-biophysics
plugin/skills/tooluniverse-computational-biophysics - tooluniverse-connect
plugin/skills/tooluniverse-connect - tooluniverse-crispr-screen-analysis
plugin/skills/tooluniverse-crispr-screen-analysis - tooluniverse-custom-tool
plugin/skills/tooluniverse-custom-tool - tooluniverse-data-integration-analysis
plugin/skills/tooluniverse-data-integration-analysis - tooluniverse-data-wrangling
plugin/skills/tooluniverse-data-wrangling - tooluniverse-database-logins
plugin/skills/tooluniverse-database-logins - tooluniverse-dataset-discovery
plugin/skills/tooluniverse-dataset-discovery - tooluniverse-diagnostic-test-evaluation
plugin/skills/tooluniverse-diagnostic-test-evaluation - tooluniverse-disease-research
plugin/skills/tooluniverse-disease-research - tooluniverse-dose-response
plugin/skills/tooluniverse-dose-response - tooluniverse-drug-drug-interaction
plugin/skills/tooluniverse-drug-drug-interaction - tooluniverse-drug-mechanism-research
plugin/skills/tooluniverse-drug-mechanism-research - tooluniverse-drug-regulatory
plugin/skills/tooluniverse-drug-regulatory - tooluniverse-drug-repurposing
plugin/skills/tooluniverse-drug-repurposing - tooluniverse-drug-research
plugin/skills/tooluniverse-drug-research - tooluniverse-drug-synergy
plugin/skills/tooluniverse-drug-synergy - tooluniverse-drug-target-validation
plugin/skills/tooluniverse-drug-target-validation - tooluniverse-ecology-biodiversity
plugin/skills/tooluniverse-ecology-biodiversity - tooluniverse-electron-microscopy
plugin/skills/tooluniverse-electron-microscopy - tooluniverse-enzyme-design
plugin/skills/tooluniverse-enzyme-design - tooluniverse-enzyme-kinetics
plugin/skills/tooluniverse-enzyme-kinetics - tooluniverse-epidemiological-analysis
plugin/skills/tooluniverse-epidemiological-analysis - tooluniverse-epigenomics
plugin/skills/tooluniverse-epigenomics - tooluniverse-epigenomics-chromatin
plugin/skills/tooluniverse-epigenomics-chromatin - tooluniverse-expression-data-retrieval
plugin/skills/tooluniverse-expression-data-retrieval - tooluniverse-fastq-qc
plugin/skills/tooluniverse-fastq-qc - tooluniverse-functional-genomics-screens
plugin/skills/tooluniverse-functional-genomics-screens - tooluniverse-gene-disease-association
plugin/skills/tooluniverse-gene-disease-association - tooluniverse-gene-enrichment
plugin/skills/tooluniverse-gene-enrichment - tooluniverse-gene-liability
plugin/skills/tooluniverse-gene-liability - tooluniverse-gene-regulatory-networks
plugin/skills/tooluniverse-gene-regulatory-networks - tooluniverse-glycobiology
plugin/skills/tooluniverse-glycobiology - tooluniverse-gpcr-structural-pharmacology
plugin/skills/tooluniverse-gpcr-structural-pharmacology - tooluniverse-gwas-drug-discovery
plugin/skills/tooluniverse-gwas-drug-discovery - tooluniverse-gwas-finemapping
plugin/skills/tooluniverse-gwas-finemapping - tooluniverse-gwas-snp-interpretation
plugin/skills/tooluniverse-gwas-snp-interpretation - tooluniverse-gwas-study-explorer
plugin/skills/tooluniverse-gwas-study-explorer - tooluniverse-gwas-trait-to-gene
plugin/skills/tooluniverse-gwas-trait-to-gene - tooluniverse-hla-immunogenomics
plugin/skills/tooluniverse-hla-immunogenomics - tooluniverse-image-analysis
plugin/skills/tooluniverse-image-analysis - tooluniverse-immune-repertoire-analysis
plugin/skills/tooluniverse-immune-repertoire-analysis - tooluniverse-immunology
plugin/skills/tooluniverse-immunology - tooluniverse-immunotherapy-response-prediction
plugin/skills/tooluniverse-immunotherapy-response-prediction - tooluniverse-infectious-disease
plugin/skills/tooluniverse-infectious-disease - tooluniverse-inorganic-physical-chemistry
plugin/skills/tooluniverse-inorganic-physical-chemistry - tooluniverse-install-skills
plugin/skills/tooluniverse-install-skills - tooluniverse-kegg-disease-drug
plugin/skills/tooluniverse-kegg-disease-drug - tooluniverse-lab-data-standardization
plugin/skills/tooluniverse-lab-data-standardization - tooluniverse-lab-protocols
plugin/skills/tooluniverse-lab-protocols - tooluniverse-lipidomics
plugin/skills/tooluniverse-lipidomics - tooluniverse-literature-deep-research
plugin/skills/tooluniverse-literature-deep-research - tooluniverse-medical-imaging-radiology
plugin/skills/tooluniverse-medical-imaging-radiology - tooluniverse-mendelian-randomization
plugin/skills/tooluniverse-mendelian-randomization - tooluniverse-meta-analysis
plugin/skills/tooluniverse-meta-analysis - tooluniverse-metabolomics
plugin/skills/tooluniverse-metabolomics - tooluniverse-metabolomics-analysis
plugin/skills/tooluniverse-metabolomics-analysis - tooluniverse-metabolomics-pathway
plugin/skills/tooluniverse-metabolomics-pathway - tooluniverse-metagenomics-analysis
plugin/skills/tooluniverse-metagenomics-analysis - tooluniverse-microbial-genome-characterization
plugin/skills/tooluniverse-microbial-genome-characterization - tooluniverse-microbiome-research
plugin/skills/tooluniverse-microbiome-research - tooluniverse-ml-inference-utility
plugin/skills/tooluniverse-ml-inference-utility - tooluniverse-model-organism-genetics
plugin/skills/tooluniverse-model-organism-genetics - tooluniverse-molecular-cloning
plugin/skills/tooluniverse-molecular-cloning - tooluniverse-multi-omics-integration
plugin/skills/tooluniverse-multi-omics-integration - tooluniverse-multiomic-disease-characterization
plugin/skills/tooluniverse-multiomic-disease-characterization - tooluniverse-natural-product-dereplication
plugin/skills/tooluniverse-natural-product-dereplication - tooluniverse-network-pharmacology
plugin/skills/tooluniverse-network-pharmacology - tooluniverse-neuroscience
plugin/skills/tooluniverse-neuroscience - tooluniverse-neurosynth-meta-analysis
plugin/skills/tooluniverse-neurosynth-meta-analysis - tooluniverse-nfcore-pipelines
plugin/skills/tooluniverse-nfcore-pipelines - tooluniverse-nih-funding-landscape
plugin/skills/tooluniverse-nih-funding-landscape - tooluniverse-noncoding-rna
plugin/skills/tooluniverse-noncoding-rna - tooluniverse-nutrition-food-composition
plugin/skills/tooluniverse-nutrition-food-composition - tooluniverse-organic-chemistry
plugin/skills/tooluniverse-organic-chemistry - tooluniverse-pathway-disease-genetics
plugin/skills/tooluniverse-pathway-disease-genetics - tooluniverse-peptide-target-deorphanization
plugin/skills/tooluniverse-peptide-target-deorphanization - tooluniverse-pharmacogenomics
plugin/skills/tooluniverse-pharmacogenomics - tooluniverse-pharmacokinetics
plugin/skills/tooluniverse-pharmacokinetics - tooluniverse-pharmacovigilance
plugin/skills/tooluniverse-pharmacovigilance - tooluniverse-phewas
plugin/skills/tooluniverse-phewas - tooluniverse-phylogenetics
plugin/skills/tooluniverse-phylogenetics - tooluniverse-plant-genomics
plugin/skills/tooluniverse-plant-genomics - tooluniverse-polygenic-risk-score
plugin/skills/tooluniverse-polygenic-risk-score - tooluniverse-population-genetics
plugin/skills/tooluniverse-population-genetics - tooluniverse-population-genetics-1000genomes
plugin/skills/tooluniverse-population-genetics-1000genomes - tooluniverse-precision-medicine-stratification
plugin/skills/tooluniverse-precision-medicine-stratification - tooluniverse-precision-oncology
plugin/skills/tooluniverse-precision-oncology - tooluniverse-primer-design
plugin/skills/tooluniverse-primer-design - tooluniverse-product-safety-surveillance
plugin/skills/tooluniverse-product-safety-surveillance - tooluniverse-protein-design-campaign
plugin/skills/tooluniverse-protein-design-campaign - tooluniverse-protein-interactions
plugin/skills/tooluniverse-protein-interactions - tooluniverse-protein-lof-mechanism
plugin/skills/tooluniverse-protein-lof-mechanism - tooluniverse-protein-modification-analysis
plugin/skills/tooluniverse-protein-modification-analysis - tooluniverse-protein-sae-variant-interpretation
plugin/skills/tooluniverse-protein-sae-variant-interpretation - tooluniverse-protein-structural-annotation-pdb
plugin/skills/tooluniverse-protein-structural-annotation-pdb - tooluniverse-protein-structure-prediction
plugin/skills/tooluniverse-protein-structure-prediction - tooluniverse-protein-structure-retrieval
plugin/skills/tooluniverse-protein-structure-retrieval - tooluniverse-protein-therapeutic-design
plugin/skills/tooluniverse-protein-therapeutic-design - tooluniverse-proteomics-analysis
plugin/skills/tooluniverse-proteomics-analysis - tooluniverse-proteomics-data-retrieval
plugin/skills/tooluniverse-proteomics-data-retrieval - tooluniverse-publish-tool
plugin/skills/tooluniverse-publish-tool - tooluniverse-rare-disease-diagnosis
plugin/skills/tooluniverse-rare-disease-diagnosis - tooluniverse-rare-disease-genomics
plugin/skills/tooluniverse-rare-disease-genomics - tooluniverse-regulatory-genomics
plugin/skills/tooluniverse-regulatory-genomics - tooluniverse-regulatory-variant-analysis
plugin/skills/tooluniverse-regulatory-variant-analysis - tooluniverse-residue-functional-mechanism-interpretation
plugin/skills/tooluniverse-residue-functional-mechanism-interpretation - tooluniverse-rnaseq-deseq2
plugin/skills/tooluniverse-rnaseq-deseq2 - tooluniverse-scientific-peer-review
plugin/skills/tooluniverse-scientific-peer-review - tooluniverse-scientific-problem-selection
plugin/skills/tooluniverse-scientific-problem-selection - tooluniverse-scvi-tools
plugin/skills/tooluniverse-scvi-tools - tooluniverse-sdk
plugin/skills/tooluniverse-sdk - tooluniverse-self-review
plugin/skills/tooluniverse-self-review - tooluniverse-sequence-analysis
plugin/skills/tooluniverse-sequence-analysis - tooluniverse-sequence-retrieval
plugin/skills/tooluniverse-sequence-retrieval - tooluniverse-share-computer
plugin/skills/tooluniverse-share-computer - tooluniverse-single-cell
plugin/skills/tooluniverse-single-cell - tooluniverse-small-molecule-discovery
plugin/skills/tooluniverse-small-molecule-discovery - tooluniverse-spatial-omics-analysis
plugin/skills/tooluniverse-spatial-omics-analysis - tooluniverse-spatial-transcriptomics
plugin/skills/tooluniverse-spatial-transcriptomics - tooluniverse-statistical-modeling
plugin/skills/tooluniverse-statistical-modeling - tooluniverse-stem-cell-organoid
plugin/skills/tooluniverse-stem-cell-organoid - tooluniverse-structural-proteomics
plugin/skills/tooluniverse-structural-proteomics - tooluniverse-structural-variant-analysis
plugin/skills/tooluniverse-structural-variant-analysis - tooluniverse-synthetic-biology-parts
plugin/skills/tooluniverse-synthetic-biology-parts - tooluniverse-systems-biology
plugin/skills/tooluniverse-systems-biology - tooluniverse-target-research
plugin/skills/tooluniverse-target-research - tooluniverse-toxicology
plugin/skills/tooluniverse-toxicology - tooluniverse-use-shared-computer
plugin/skills/tooluniverse-use-shared-computer - tooluniverse-vaccine-design
plugin/skills/tooluniverse-vaccine-design - tooluniverse-variant-analysis
plugin/skills/tooluniverse-variant-analysis - tooluniverse-variant-functional-annotation
plugin/skills/tooluniverse-variant-functional-annotation - tooluniverse-variant-interpretation
plugin/skills/tooluniverse-variant-interpretation - tooluniverse-variant-predictor-dms-validation
plugin/skills/tooluniverse-variant-predictor-dms-validation - tooluniverse-variant-to-mechanism
plugin/skills/tooluniverse-variant-to-mechanism - setup-tooluniverse
plugins/tooluniverse/skills/setup-tooluniverse - tooluniverse
plugins/tooluniverse/skills/tooluniverse - tooluniverse-acmg-variant-classification
plugins/tooluniverse/skills/tooluniverse-acmg-variant-classification - tooluniverse-admet-prediction
plugins/tooluniverse/skills/tooluniverse-admet-prediction - tooluniverse-adverse-event-detection
plugins/tooluniverse/skills/tooluniverse-adverse-event-detection - tooluniverse-adverse-outcome-pathway
plugins/tooluniverse/skills/tooluniverse-adverse-outcome-pathway - tooluniverse-aging-senescence
plugins/tooluniverse/skills/tooluniverse-aging-senescence - tooluniverse-antibody-engineering
plugins/tooluniverse/skills/tooluniverse-antibody-engineering - tooluniverse-antigravity-plugin
plugins/tooluniverse/skills/tooluniverse-antigravity-plugin - tooluniverse-binder-discovery
plugins/tooluniverse/skills/tooluniverse-binder-discovery - tooluniverse-bioinformatics-resource-discovery
plugins/tooluniverse/skills/tooluniverse-bioinformatics-resource-discovery - tooluniverse-biomedical-fact-lookup
plugins/tooluniverse/skills/tooluniverse-biomedical-fact-lookup - tooluniverse-cancer-classification
plugins/tooluniverse/skills/tooluniverse-cancer-classification - tooluniverse-cancer-genomics-tcga
plugins/tooluniverse/skills/tooluniverse-cancer-genomics-tcga - tooluniverse-cancer-variant-interpretation
plugins/tooluniverse/skills/tooluniverse-cancer-variant-interpretation - tooluniverse-cell-line-profiling
plugins/tooluniverse/skills/tooluniverse-cell-line-profiling - tooluniverse-chemical-compound-retrieval
plugins/tooluniverse/skills/tooluniverse-chemical-compound-retrieval - tooluniverse-chemical-safety
plugins/tooluniverse/skills/tooluniverse-chemical-safety - tooluniverse-chemical-sourcing
plugins/tooluniverse/skills/tooluniverse-chemical-sourcing - tooluniverse-clinical-data-integration
plugins/tooluniverse/skills/tooluniverse-clinical-data-integration - tooluniverse-clinical-guidelines
plugins/tooluniverse/skills/tooluniverse-clinical-guidelines - tooluniverse-clinical-risk-scoring
plugins/tooluniverse/skills/tooluniverse-clinical-risk-scoring - tooluniverse-clinical-terminology-lookup
plugins/tooluniverse/skills/tooluniverse-clinical-terminology-lookup - tooluniverse-clinical-trial-design
plugins/tooluniverse/skills/tooluniverse-clinical-trial-design - tooluniverse-clinical-trial-matching
plugins/tooluniverse/skills/tooluniverse-clinical-trial-matching - tooluniverse-clinical-trial-protocol
plugins/tooluniverse/skills/tooluniverse-clinical-trial-protocol - tooluniverse-codex-plugin
plugins/tooluniverse/skills/tooluniverse-codex-plugin - tooluniverse-comparative-genomics
plugins/tooluniverse/skills/tooluniverse-comparative-genomics - tooluniverse-computational-biophysics
plugins/tooluniverse/skills/tooluniverse-computational-biophysics - tooluniverse-connect
plugins/tooluniverse/skills/tooluniverse-connect - tooluniverse-crispr-screen-analysis
plugins/tooluniverse/skills/tooluniverse-crispr-screen-analysis - tooluniverse-custom-tool
plugins/tooluniverse/skills/tooluniverse-custom-tool - tooluniverse-data-integration-analysis
plugins/tooluniverse/skills/tooluniverse-data-integration-analysis - tooluniverse-data-wrangling
plugins/tooluniverse/skills/tooluniverse-data-wrangling - tooluniverse-database-logins
plugins/tooluniverse/skills/tooluniverse-database-logins - tooluniverse-dataset-discovery
plugins/tooluniverse/skills/tooluniverse-dataset-discovery - tooluniverse-diagnostic-test-evaluation
plugins/tooluniverse/skills/tooluniverse-diagnostic-test-evaluation - tooluniverse-disease-research
plugins/tooluniverse/skills/tooluniverse-disease-research - tooluniverse-dose-response
plugins/tooluniverse/skills/tooluniverse-dose-response - tooluniverse-drug-drug-interaction
plugins/tooluniverse/skills/tooluniverse-drug-drug-interaction - tooluniverse-drug-mechanism-research
plugins/tooluniverse/skills/tooluniverse-drug-mechanism-research - tooluniverse-drug-regulatory
plugins/tooluniverse/skills/tooluniverse-drug-regulatory - tooluniverse-drug-repurposing
plugins/tooluniverse/skills/tooluniverse-drug-repurposing - tooluniverse-drug-research
plugins/tooluniverse/skills/tooluniverse-drug-research - tooluniverse-drug-synergy
plugins/tooluniverse/skills/tooluniverse-drug-synergy - tooluniverse-drug-target-validation
plugins/tooluniverse/skills/tooluniverse-drug-target-validation - tooluniverse-ecology-biodiversity
plugins/tooluniverse/skills/tooluniverse-ecology-biodiversity - tooluniverse-electron-microscopy
plugins/tooluniverse/skills/tooluniverse-electron-microscopy - tooluniverse-enzyme-design
plugins/tooluniverse/skills/tooluniverse-enzyme-design - tooluniverse-enzyme-kinetics
plugins/tooluniverse/skills/tooluniverse-enzyme-kinetics - tooluniverse-epidemiological-analysis
plugins/tooluniverse/skills/tooluniverse-epidemiological-analysis - tooluniverse-epigenomics
plugins/tooluniverse/skills/tooluniverse-epigenomics - tooluniverse-epigenomics-chromatin
plugins/tooluniverse/skills/tooluniverse-epigenomics-chromatin - tooluniverse-expression-data-retrieval
plugins/tooluniverse/skills/tooluniverse-expression-data-retrieval - tooluniverse-fastq-qc
plugins/tooluniverse/skills/tooluniverse-fastq-qc - tooluniverse-functional-genomics-screens
plugins/tooluniverse/skills/tooluniverse-functional-genomics-screens - tooluniverse-gene-disease-association
plugins/tooluniverse/skills/tooluniverse-gene-disease-association - tooluniverse-gene-enrichment
plugins/tooluniverse/skills/tooluniverse-gene-enrichment - tooluniverse-gene-liability
plugins/tooluniverse/skills/tooluniverse-gene-liability - tooluniverse-gene-regulatory-networks
plugins/tooluniverse/skills/tooluniverse-gene-regulatory-networks - tooluniverse-glycobiology
plugins/tooluniverse/skills/tooluniverse-glycobiology - tooluniverse-gpcr-structural-pharmacology
plugins/tooluniverse/skills/tooluniverse-gpcr-structural-pharmacology - tooluniverse-gwas-drug-discovery
plugins/tooluniverse/skills/tooluniverse-gwas-drug-discovery - tooluniverse-gwas-finemapping
plugins/tooluniverse/skills/tooluniverse-gwas-finemapping - tooluniverse-gwas-snp-interpretation
plugins/tooluniverse/skills/tooluniverse-gwas-snp-interpretation - tooluniverse-gwas-study-explorer
plugins/tooluniverse/skills/tooluniverse-gwas-study-explorer - tooluniverse-gwas-trait-to-gene
plugins/tooluniverse/skills/tooluniverse-gwas-trait-to-gene - tooluniverse-hla-immunogenomics
plugins/tooluniverse/skills/tooluniverse-hla-immunogenomics - tooluniverse-image-analysis
plugins/tooluniverse/skills/tooluniverse-image-analysis - tooluniverse-immune-repertoire-analysis
plugins/tooluniverse/skills/tooluniverse-immune-repertoire-analysis - tooluniverse-immunology
plugins/tooluniverse/skills/tooluniverse-immunology - tooluniverse-immunotherapy-response-prediction
plugins/tooluniverse/skills/tooluniverse-immunotherapy-response-prediction - tooluniverse-infectious-disease
plugins/tooluniverse/skills/tooluniverse-infectious-disease - tooluniverse-inorganic-physical-chemistry
plugins/tooluniverse/skills/tooluniverse-inorganic-physical-chemistry - tooluniverse-install-skills
plugins/tooluniverse/skills/tooluniverse-install-skills - tooluniverse-kegg-disease-drug
plugins/tooluniverse/skills/tooluniverse-kegg-disease-drug - tooluniverse-lab-data-standardization
plugins/tooluniverse/skills/tooluniverse-lab-data-standardization - tooluniverse-lab-protocols
plugins/tooluniverse/skills/tooluniverse-lab-protocols - tooluniverse-lipidomics
plugins/tooluniverse/skills/tooluniverse-lipidomics - tooluniverse-literature-deep-research
plugins/tooluniverse/skills/tooluniverse-literature-deep-research - tooluniverse-medical-imaging-radiology
plugins/tooluniverse/skills/tooluniverse-medical-imaging-radiology - tooluniverse-mendelian-randomization
plugins/tooluniverse/skills/tooluniverse-mendelian-randomization - tooluniverse-meta-analysis
plugins/tooluniverse/skills/tooluniverse-meta-analysis - tooluniverse-metabolomics
plugins/tooluniverse/skills/tooluniverse-metabolomics - tooluniverse-metabolomics-analysis
plugins/tooluniverse/skills/tooluniverse-metabolomics-analysis - tooluniverse-metabolomics-pathway
plugins/tooluniverse/skills/tooluniverse-metabolomics-pathway - tooluniverse-metagenomics-analysis
plugins/tooluniverse/skills/tooluniverse-metagenomics-analysis - tooluniverse-microbial-genome-characterization
plugins/tooluniverse/skills/tooluniverse-microbial-genome-characterization - tooluniverse-microbiome-research
plugins/tooluniverse/skills/tooluniverse-microbiome-research - tooluniverse-ml-inference-utility
plugins/tooluniverse/skills/tooluniverse-ml-inference-utility - tooluniverse-model-organism-genetics
plugins/tooluniverse/skills/tooluniverse-model-organism-genetics - tooluniverse-molecular-cloning
plugins/tooluniverse/skills/tooluniverse-molecular-cloning - tooluniverse-multi-omics-integration
plugins/tooluniverse/skills/tooluniverse-multi-omics-integration - tooluniverse-multiomic-disease-characterization
plugins/tooluniverse/skills/tooluniverse-multiomic-disease-characterization - tooluniverse-natural-product-dereplication
plugins/tooluniverse/skills/tooluniverse-natural-product-dereplication - tooluniverse-network-pharmacology
plugins/tooluniverse/skills/tooluniverse-network-pharmacology - tooluniverse-neuroscience
plugins/tooluniverse/skills/tooluniverse-neuroscience - tooluniverse-neurosynth-meta-analysis
plugins/tooluniverse/skills/tooluniverse-neurosynth-meta-analysis - tooluniverse-nfcore-pipelines
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines - tooluniverse-nih-funding-landscape
plugins/tooluniverse/skills/tooluniverse-nih-funding-landscape - tooluniverse-noncoding-rna
plugins/tooluniverse/skills/tooluniverse-noncoding-rna - tooluniverse-nutrition-food-composition
plugins/tooluniverse/skills/tooluniverse-nutrition-food-composition - tooluniverse-organic-chemistry
plugins/tooluniverse/skills/tooluniverse-organic-chemistry - tooluniverse-pathway-disease-genetics
plugins/tooluniverse/skills/tooluniverse-pathway-disease-genetics - tooluniverse-peptide-target-deorphanization
plugins/tooluniverse/skills/tooluniverse-peptide-target-deorphanization - tooluniverse-pharmacogenomics
plugins/tooluniverse/skills/tooluniverse-pharmacogenomics - tooluniverse-pharmacokinetics
plugins/tooluniverse/skills/tooluniverse-pharmacokinetics - tooluniverse-pharmacovigilance
plugins/tooluniverse/skills/tooluniverse-pharmacovigilance - tooluniverse-phewas
plugins/tooluniverse/skills/tooluniverse-phewas - tooluniverse-phylogenetics
plugins/tooluniverse/skills/tooluniverse-phylogenetics - tooluniverse-plant-genomics
plugins/tooluniverse/skills/tooluniverse-plant-genomics - tooluniverse-polygenic-risk-score
plugins/tooluniverse/skills/tooluniverse-polygenic-risk-score - tooluniverse-population-genetics
plugins/tooluniverse/skills/tooluniverse-population-genetics - tooluniverse-population-genetics-1000genomes
plugins/tooluniverse/skills/tooluniverse-population-genetics-1000genomes - tooluniverse-precision-medicine-stratification
plugins/tooluniverse/skills/tooluniverse-precision-medicine-stratification - tooluniverse-precision-oncology
plugins/tooluniverse/skills/tooluniverse-precision-oncology - tooluniverse-primer-design
plugins/tooluniverse/skills/tooluniverse-primer-design - tooluniverse-product-safety-surveillance
plugins/tooluniverse/skills/tooluniverse-product-safety-surveillance - tooluniverse-protein-design-campaign
plugins/tooluniverse/skills/tooluniverse-protein-design-campaign - tooluniverse-protein-interactions
plugins/tooluniverse/skills/tooluniverse-protein-interactions - tooluniverse-protein-lof-mechanism
plugins/tooluniverse/skills/tooluniverse-protein-lof-mechanism - tooluniverse-protein-modification-analysis
plugins/tooluniverse/skills/tooluniverse-protein-modification-analysis - tooluniverse-protein-sae-variant-interpretation
plugins/tooluniverse/skills/tooluniverse-protein-sae-variant-interpretation - tooluniverse-protein-structural-annotation-pdb
plugins/tooluniverse/skills/tooluniverse-protein-structural-annotation-pdb - tooluniverse-protein-structure-prediction
plugins/tooluniverse/skills/tooluniverse-protein-structure-prediction - tooluniverse-protein-structure-retrieval
plugins/tooluniverse/skills/tooluniverse-protein-structure-retrieval - tooluniverse-protein-therapeutic-design
plugins/tooluniverse/skills/tooluniverse-protein-therapeutic-design - tooluniverse-proteomics-analysis
plugins/tooluniverse/skills/tooluniverse-proteomics-analysis - tooluniverse-proteomics-data-retrieval
plugins/tooluniverse/skills/tooluniverse-proteomics-data-retrieval - tooluniverse-publish-tool
plugins/tooluniverse/skills/tooluniverse-publish-tool - tooluniverse-rare-disease-diagnosis
plugins/tooluniverse/skills/tooluniverse-rare-disease-diagnosis - tooluniverse-rare-disease-genomics
plugins/tooluniverse/skills/tooluniverse-rare-disease-genomics - tooluniverse-regulatory-genomics
plugins/tooluniverse/skills/tooluniverse-regulatory-genomics - tooluniverse-regulatory-variant-analysis
plugins/tooluniverse/skills/tooluniverse-regulatory-variant-analysis - tooluniverse-residue-functional-mechanism-interpretation
plugins/tooluniverse/skills/tooluniverse-residue-functional-mechanism-interpretation - tooluniverse-rnaseq-deseq2
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2 - tooluniverse-scientific-peer-review
plugins/tooluniverse/skills/tooluniverse-scientific-peer-review - tooluniverse-scientific-problem-selection
plugins/tooluniverse/skills/tooluniverse-scientific-problem-selection - tooluniverse-scvi-tools
plugins/tooluniverse/skills/tooluniverse-scvi-tools - tooluniverse-sdk
plugins/tooluniverse/skills/tooluniverse-sdk - tooluniverse-self-review
plugins/tooluniverse/skills/tooluniverse-self-review - tooluniverse-sequence-analysis
plugins/tooluniverse/skills/tooluniverse-sequence-analysis - tooluniverse-sequence-retrieval
plugins/tooluniverse/skills/tooluniverse-sequence-retrieval - tooluniverse-share-computer
plugins/tooluniverse/skills/tooluniverse-share-computer - tooluniverse-single-cell
plugins/tooluniverse/skills/tooluniverse-single-cell - tooluniverse-small-molecule-discovery
plugins/tooluniverse/skills/tooluniverse-small-molecule-discovery - tooluniverse-spatial-omics-analysis
plugins/tooluniverse/skills/tooluniverse-spatial-omics-analysis - tooluniverse-spatial-transcriptomics
plugins/tooluniverse/skills/tooluniverse-spatial-transcriptomics - tooluniverse-statistical-modeling
plugins/tooluniverse/skills/tooluniverse-statistical-modeling - tooluniverse-stem-cell-organoid
plugins/tooluniverse/skills/tooluniverse-stem-cell-organoid - tooluniverse-structural-proteomics
plugins/tooluniverse/skills/tooluniverse-structural-proteomics - tooluniverse-structural-variant-analysis
plugins/tooluniverse/skills/tooluniverse-structural-variant-analysis - tooluniverse-synthetic-biology-parts
plugins/tooluniverse/skills/tooluniverse-synthetic-biology-parts - tooluniverse-systems-biology
plugins/tooluniverse/skills/tooluniverse-systems-biology - tooluniverse-target-research
plugins/tooluniverse/skills/tooluniverse-target-research - tooluniverse-toxicology
plugins/tooluniverse/skills/tooluniverse-toxicology - tooluniverse-use-shared-computer
plugins/tooluniverse/skills/tooluniverse-use-shared-computer - tooluniverse-vaccine-design
plugins/tooluniverse/skills/tooluniverse-vaccine-design - tooluniverse-variant-analysis
plugins/tooluniverse/skills/tooluniverse-variant-analysis - tooluniverse-variant-functional-annotation
plugins/tooluniverse/skills/tooluniverse-variant-functional-annotation - tooluniverse-variant-interpretation
plugins/tooluniverse/skills/tooluniverse-variant-interpretation - tooluniverse-variant-predictor-dms-validation
plugins/tooluniverse/skills/tooluniverse-variant-predictor-dms-validation - tooluniverse-variant-to-mechanism
plugins/tooluniverse/skills/tooluniverse-variant-to-mechanism - create-tooluniverse-skill
skills/create-tooluniverse-skill - tooluniverse-[domain-name]
skills/create-tooluniverse-skill/assets/skill_template - devtu-benchmark-harness
skills/devtu-benchmark-harness - host-and-share-remote-tool
skills/host-and-share-remote-tool - setup-af2ig-remote-tool
skills/setup-af2ig-remote-tool - setup-af3_jax-remote-tool
skills/setup-af3_jax-remote-tool - setup-af3_torch-remote-tool
skills/setup-af3_torch-remote-tool - setup-apnovo-remote-tool
skills/setup-apnovo-remote-tool - setup-atlasfold-remote-tool
skills/setup-atlasfold-remote-tool - setup-bioemu2-remote-tool
skills/setup-bioemu2-remote-tool - setup-boltz-remote-tool
skills/setup-boltz-remote-tool - setup-boltzgen-remote-tool
skills/setup-boltzgen-remote-tool - setup-borzoi-remote-tool
skills/setup-borzoi-remote-tool - setup-borzoi_calico-remote-tool
skills/setup-borzoi_calico-remote-tool - setup-cell2location-remote-tool
skills/setup-cell2location-remote-tool - setup-cellrank-remote-tool
skills/setup-cellrank-remote-tool - setup-celltypist-remote-tool
skills/setup-celltypist-remote-tool - setup-chai1-remote-tool
skills/setup-chai1-remote-tool - setup-chrombpnet-remote-tool
skills/setup-chrombpnet-remote-tool - setup-colabdesign-remote-tool
skills/setup-colabdesign-remote-tool - setup-colabfold-remote-tool
skills/setup-colabfold-remote-tool - setup-complexa-remote-tool
skills/setup-complexa-remote-tool - setup-depmap-24q2-remote-tool
skills/setup-depmap-24q2-remote-tool - setup-e1-remote-tool
skills/setup-e1-remote-tool - setup-ef2inv-remote-tool
skills/setup-ef2inv-remote-tool - setup-enformer-remote-tool
skills/setup-enformer-remote-tool - setup-enformer_deepmind-remote-tool
skills/setup-enformer_deepmind-remote-tool - setup-esm-remote-tool
skills/setup-esm-remote-tool - setup-esm_if1-remote-tool
skills/setup-esm_if1-remote-tool - setup-esmc-remote-tool
skills/setup-esmc-remote-tool - setup-esmfold2-remote-tool
skills/setup-esmfold2-remote-tool - setup-evo2-remote-tool
skills/setup-evo2-remote-tool - setup-expert-feedback-remote-tool
skills/setup-expert-feedback-remote-tool - setup-genie3-remote-tool
skills/setup-genie3-remote-tool - setup-gpnstar-remote-tool
skills/setup-gpnstar-remote-tool - setup-harmony-remote-tool
skills/setup-harmony-remote-tool - setup-immune-compass-remote-tool
skills/setup-immune-compass-remote-tool - setup-ldsc-remote-tool
skills/setup-ldsc-remote-tool - setup-liana-remote-tool
skills/setup-liana-remote-tool - setup-macs3-remote-tool
skills/setup-macs3-remote-tool - setup-milo-remote-tool
skills/setup-milo-remote-tool - setup-mofa-remote-tool
skills/setup-mofa-remote-tool - setup-monocle3-remote-tool
skills/setup-monocle3-remote-tool - setup-mosaic-remote-tool
skills/setup-mosaic-remote-tool - setup-opendde-remote-tool
skills/setup-opendde-remote-tool - setup-openfold3-remote-tool
skills/setup-openfold3-remote-tool - setup-openfold3_ob0-remote-tool
skills/setup-openfold3_ob0-remote-tool - setup-paga-remote-tool
skills/setup-paga-remote-tool - setup-pinnacle-remote-tool
skills/setup-pinnacle-remote-tool - setup-progen2-remote-tool
skills/setup-progen2-remote-tool - setup-proteinmpnn-remote-tool
skills/setup-proteinmpnn-remote-tool - setup-protenix_v1-remote-tool
skills/setup-protenix_v1-remote-tool - setup-rfd4_proteina-remote-tool
skills/setup-rfd4_proteina-remote-tool - setup-rfdiffusion3-remote-tool
skills/setup-rfdiffusion3-remote-tool - setup-rosettafold3-remote-tool
skills/setup-rosettafold3-remote-tool - setup-scanvi-remote-tool
skills/setup-scanvi-remote-tool - setup-scrublet-remote-tool
skills/setup-scrublet-remote-tool - setup-scvelo-remote-tool
skills/setup-scvelo-remote-tool - setup-scvi-remote-tool
skills/setup-scvi-remote-tool - setup-singler-remote-tool
skills/setup-singler-remote-tool - setup-slingshot-remote-tool
skills/setup-slingshot-remote-tool - setup-squidpy-remote-tool
skills/setup-squidpy-remote-tool - setup-tangram-remote-tool
skills/setup-tangram-remote-tool - setup-tooluniverse
skills/setup-tooluniverse - setup-transcriptformer-remote-tool
skills/setup-transcriptformer-remote-tool - setup-uspto-downloader-remote-tool
skills/setup-uspto-downloader-remote-tool - tooluniverse
skills/tooluniverse - tooluniverse-acmg-variant-classification
skills/tooluniverse-acmg-variant-classification - tooluniverse-admet-prediction
skills/tooluniverse-admet-prediction - tooluniverse-adverse-event-detection
skills/tooluniverse-adverse-event-detection - tooluniverse-adverse-outcome-pathway
skills/tooluniverse-adverse-outcome-pathway - tooluniverse-aging-senescence
skills/tooluniverse-aging-senescence - tooluniverse-antibody-engineering
skills/tooluniverse-antibody-engineering - tooluniverse-antigravity-plugin
skills/tooluniverse-antigravity-plugin - tooluniverse-binder-discovery
skills/tooluniverse-binder-discovery - tooluniverse-bioinformatics-resource-discovery
skills/tooluniverse-bioinformatics-resource-discovery - tooluniverse-biomedical-fact-lookup
skills/tooluniverse-biomedical-fact-lookup - tooluniverse-cancer-classification
skills/tooluniverse-cancer-classification - tooluniverse-cancer-genomics-tcga
skills/tooluniverse-cancer-genomics-tcga - tooluniverse-cancer-variant-interpretation
skills/tooluniverse-cancer-variant-interpretation - tooluniverse-cell-line-profiling
skills/tooluniverse-cell-line-profiling - tooluniverse-chemical-compound-retrieval
skills/tooluniverse-chemical-compound-retrieval - tooluniverse-chemical-safety
skills/tooluniverse-chemical-safety - tooluniverse-chemical-sourcing
skills/tooluniverse-chemical-sourcing - tooluniverse-claude-code-plugin
skills/tooluniverse-claude-code-plugin - tooluniverse-clinical-data-integration
skills/tooluniverse-clinical-data-integration - tooluniverse-clinical-guidelines
skills/tooluniverse-clinical-guidelines - tooluniverse-clinical-risk-scoring
skills/tooluniverse-clinical-risk-scoring - tooluniverse-clinical-terminology-lookup
skills/tooluniverse-clinical-terminology-lookup - tooluniverse-clinical-trial-design
skills/tooluniverse-clinical-trial-design - tooluniverse-clinical-trial-matching
skills/tooluniverse-clinical-trial-matching - tooluniverse-clinical-trial-protocol
skills/tooluniverse-clinical-trial-protocol - tooluniverse-codex-plugin
skills/tooluniverse-codex-plugin - tooluniverse-comparative-genomics
skills/tooluniverse-comparative-genomics - tooluniverse-computational-biophysics
skills/tooluniverse-computational-biophysics - tooluniverse-connect
skills/tooluniverse-connect - tooluniverse-crispr-screen-analysis
skills/tooluniverse-crispr-screen-analysis - tooluniverse-cs-setup
skills/tooluniverse-cs-setup - tooluniverse-custom-tool
skills/tooluniverse-custom-tool - tooluniverse-data-integration-analysis
skills/tooluniverse-data-integration-analysis - tooluniverse-data-wrangling
skills/tooluniverse-data-wrangling - tooluniverse-database-logins
skills/tooluniverse-database-logins - tooluniverse-dataset-discovery
skills/tooluniverse-dataset-discovery - tooluniverse-diagnostic-test-evaluation
skills/tooluniverse-diagnostic-test-evaluation - tooluniverse-disease-research
skills/tooluniverse-disease-research - tooluniverse-dose-response
skills/tooluniverse-dose-response - tooluniverse-drug-drug-interaction
skills/tooluniverse-drug-drug-interaction - tooluniverse-drug-mechanism-research
skills/tooluniverse-drug-mechanism-research - tooluniverse-drug-regulatory
skills/tooluniverse-drug-regulatory - tooluniverse-drug-repurposing
skills/tooluniverse-drug-repurposing - tooluniverse-drug-research
skills/tooluniverse-drug-research - tooluniverse-drug-synergy
skills/tooluniverse-drug-synergy - tooluniverse-drug-target-validation
skills/tooluniverse-drug-target-validation - tooluniverse-ecology-biodiversity
skills/tooluniverse-ecology-biodiversity - tooluniverse-electron-microscopy
skills/tooluniverse-electron-microscopy - tooluniverse-enzyme-design
skills/tooluniverse-enzyme-design - tooluniverse-enzyme-kinetics
skills/tooluniverse-enzyme-kinetics - tooluniverse-epidemiological-analysis
skills/tooluniverse-epidemiological-analysis - tooluniverse-epigenomics
skills/tooluniverse-epigenomics - tooluniverse-epigenomics-chromatin
skills/tooluniverse-epigenomics-chromatin - tooluniverse-expression-data-retrieval
skills/tooluniverse-expression-data-retrieval - tooluniverse-fastq-qc
skills/tooluniverse-fastq-qc - tooluniverse-functional-genomics-screens
skills/tooluniverse-functional-genomics-screens - tooluniverse-gene-disease-association
skills/tooluniverse-gene-disease-association - tooluniverse-gene-enrichment
skills/tooluniverse-gene-enrichment - tooluniverse-gene-liability
skills/tooluniverse-gene-liability - tooluniverse-gene-regulatory-networks
skills/tooluniverse-gene-regulatory-networks - tooluniverse-glycobiology
skills/tooluniverse-glycobiology - tooluniverse-gpcr-structural-pharmacology
skills/tooluniverse-gpcr-structural-pharmacology - tooluniverse-gwas-drug-discovery
skills/tooluniverse-gwas-drug-discovery - tooluniverse-gwas-finemapping
skills/tooluniverse-gwas-finemapping - tooluniverse-gwas-snp-interpretation
skills/tooluniverse-gwas-snp-interpretation - tooluniverse-gwas-study-explorer
skills/tooluniverse-gwas-study-explorer - tooluniverse-gwas-trait-to-gene
skills/tooluniverse-gwas-trait-to-gene - tooluniverse-hla-immunogenomics
skills/tooluniverse-hla-immunogenomics - tooluniverse-image-analysis
skills/tooluniverse-image-analysis - tooluniverse-immune-repertoire-analysis
skills/tooluniverse-immune-repertoire-analysis - tooluniverse-immunology
skills/tooluniverse-immunology - tooluniverse-immunotherapy-response-prediction
skills/tooluniverse-immunotherapy-response-prediction - tooluniverse-infectious-disease
skills/tooluniverse-infectious-disease - tooluniverse-inorganic-physical-chemistry
skills/tooluniverse-inorganic-physical-chemistry - tooluniverse-install-skills
skills/tooluniverse-install-skills - tooluniverse-kegg-disease-drug
skills/tooluniverse-kegg-disease-drug - tooluniverse-lab-data-standardization
skills/tooluniverse-lab-data-standardization - tooluniverse-lab-protocols
skills/tooluniverse-lab-protocols - tooluniverse-lipidomics
skills/tooluniverse-lipidomics - tooluniverse-literature-deep-research
skills/tooluniverse-literature-deep-research - tooluniverse-medical-imaging-radiology
skills/tooluniverse-medical-imaging-radiology - tooluniverse-mendelian-randomization
skills/tooluniverse-mendelian-randomization - tooluniverse-meta-analysis
skills/tooluniverse-meta-analysis - tooluniverse-metabolomics
skills/tooluniverse-metabolomics - tooluniverse-metabolomics-analysis
skills/tooluniverse-metabolomics-analysis - tooluniverse-metabolomics-pathway
skills/tooluniverse-metabolomics-pathway - tooluniverse-metagenomics-analysis
skills/tooluniverse-metagenomics-analysis - tooluniverse-microbial-genome-characterization
skills/tooluniverse-microbial-genome-characterization - tooluniverse-microbiome-research
skills/tooluniverse-microbiome-research - tooluniverse-ml-inference-utility
skills/tooluniverse-ml-inference-utility - tooluniverse-model-organism-genetics
skills/tooluniverse-model-organism-genetics - tooluniverse-molecular-cloning
skills/tooluniverse-molecular-cloning - tooluniverse-multi-omics-integration
skills/tooluniverse-multi-omics-integration - tooluniverse-multiomic-disease-characterization
skills/tooluniverse-multiomic-disease-characterization - tooluniverse-natural-product-dereplication
skills/tooluniverse-natural-product-dereplication - tooluniverse-network-pharmacology
skills/tooluniverse-network-pharmacology - tooluniverse-neuroscience
skills/tooluniverse-neuroscience - tooluniverse-neurosynth-meta-analysis
skills/tooluniverse-neurosynth-meta-analysis - tooluniverse-nfcore-pipelines
skills/tooluniverse-nfcore-pipelines - tooluniverse-nih-funding-landscape
skills/tooluniverse-nih-funding-landscape - tooluniverse-noncoding-rna
skills/tooluniverse-noncoding-rna - tooluniverse-nutrition-food-composition
skills/tooluniverse-nutrition-food-composition - tooluniverse-organic-chemistry
skills/tooluniverse-organic-chemistry - tooluniverse-pathway-disease-genetics
skills/tooluniverse-pathway-disease-genetics - tooluniverse-peptide-target-deorphanization
skills/tooluniverse-peptide-target-deorphanization - tooluniverse-pharmacogenomics
skills/tooluniverse-pharmacogenomics - tooluniverse-pharmacokinetics
skills/tooluniverse-pharmacokinetics - tooluniverse-pharmacovigilance
skills/tooluniverse-pharmacovigilance - tooluniverse-phewas
skills/tooluniverse-phewas - tooluniverse-phylogenetics
skills/tooluniverse-phylogenetics - tooluniverse-plant-genomics
skills/tooluniverse-plant-genomics - tooluniverse-polygenic-risk-score
skills/tooluniverse-polygenic-risk-score - tooluniverse-population-genetics
skills/tooluniverse-population-genetics - tooluniverse-population-genetics-1000genomes
skills/tooluniverse-population-genetics-1000genomes - tooluniverse-precision-medicine-stratification
skills/tooluniverse-precision-medicine-stratification - tooluniverse-precision-oncology
skills/tooluniverse-precision-oncology - tooluniverse-primer-design
skills/tooluniverse-primer-design - tooluniverse-product-safety-surveillance
skills/tooluniverse-product-safety-surveillance - tooluniverse-protein-design-campaign
skills/tooluniverse-protein-design-campaign - tooluniverse-protein-interactions
skills/tooluniverse-protein-interactions - tooluniverse-protein-lof-mechanism
skills/tooluniverse-protein-lof-mechanism - tooluniverse-protein-modification-analysis
skills/tooluniverse-protein-modification-analysis - tooluniverse-protein-sae-variant-interpretation
skills/tooluniverse-protein-sae-variant-interpretation - tooluniverse-protein-structural-annotation-pdb
skills/tooluniverse-protein-structural-annotation-pdb - tooluniverse-protein-structure-prediction
skills/tooluniverse-protein-structure-prediction - tooluniverse-protein-structure-retrieval
skills/tooluniverse-protein-structure-retrieval - tooluniverse-protein-therapeutic-design
skills/tooluniverse-protein-therapeutic-design - tooluniverse-proteomics-analysis
skills/tooluniverse-proteomics-analysis - tooluniverse-proteomics-data-retrieval
skills/tooluniverse-proteomics-data-retrieval - tooluniverse-publish-tool
skills/tooluniverse-publish-tool - tooluniverse-rare-disease-diagnosis
skills/tooluniverse-rare-disease-diagnosis - tooluniverse-rare-disease-genomics
skills/tooluniverse-rare-disease-genomics - tooluniverse-regulatory-genomics
skills/tooluniverse-regulatory-genomics - tooluniverse-regulatory-variant-analysis
skills/tooluniverse-regulatory-variant-analysis - tooluniverse-residue-functional-mechanism-interpretation
skills/tooluniverse-residue-functional-mechanism-interpretation - tooluniverse-rnaseq-deseq2
skills/tooluniverse-rnaseq-deseq2 - tooluniverse-scientific-peer-review
skills/tooluniverse-scientific-peer-review - tooluniverse-scientific-problem-selection
skills/tooluniverse-scientific-problem-selection - tooluniverse-scvi-tools
skills/tooluniverse-scvi-tools - tooluniverse-sdk
skills/tooluniverse-sdk - tooluniverse-self-review
skills/tooluniverse-self-review - tooluniverse-sequence-analysis
skills/tooluniverse-sequence-analysis - tooluniverse-sequence-retrieval
skills/tooluniverse-sequence-retrieval - tooluniverse-share-computer
skills/tooluniverse-share-computer - tooluniverse-single-cell
skills/tooluniverse-single-cell - tooluniverse-small-molecule-discovery
skills/tooluniverse-small-molecule-discovery - tooluniverse-spatial-omics-analysis
skills/tooluniverse-spatial-omics-analysis - tooluniverse-spatial-transcriptomics
skills/tooluniverse-spatial-transcriptomics - tooluniverse-statistical-modeling
skills/tooluniverse-statistical-modeling - tooluniverse-stem-cell-organoid
skills/tooluniverse-stem-cell-organoid - tooluniverse-structural-proteomics
skills/tooluniverse-structural-proteomics - tooluniverse-structural-variant-analysis
skills/tooluniverse-structural-variant-analysis - tooluniverse-synthetic-biology-parts
skills/tooluniverse-synthetic-biology-parts - tooluniverse-systems-biology
skills/tooluniverse-systems-biology - tooluniverse-target-research
skills/tooluniverse-target-research - tooluniverse-toxicology
skills/tooluniverse-toxicology - tooluniverse-use-shared-computer
skills/tooluniverse-use-shared-computer - tooluniverse-vaccine-design
skills/tooluniverse-vaccine-design - tooluniverse-variant-analysis
skills/tooluniverse-variant-analysis - tooluniverse-variant-functional-annotation
skills/tooluniverse-variant-functional-annotation - tooluniverse-variant-interpretation
skills/tooluniverse-variant-interpretation - tooluniverse-variant-predictor-dms-validation
skills/tooluniverse-variant-predictor-dms-validation - tooluniverse-variant-to-mechanism
skills/tooluniverse-variant-to-mechanism
How it works
setup-tooluniverse
When your agent uses it
Install and configure ToolUniverse for any use case — MCP server (chat-based), CLI (command line with 14 subcommands), or Python SDK (Coding API with 3 calling patterns). Covers uv/uvx setup, MCP configuration for 12+ AI clients (Cursor, Claude Desktop, Windsurf, VS Code, Codex, Gemini CLI, Trae, Cline, etc.), full CLI reference (tu list/grep/info/find/run/test/status/build/remote/doctor/serve/connect/connections/disconnect), Coding API quickstart, agentic tools, code executor, API key walkthrough, skill installation, and upgrading. Use when user asks how to set up ToolUniverse, which access mode to use (MCP vs CLI vs SDK), configuring MCP servers, using the CLI, troubleshooting installation, upgrading, or mentions installing ToolUniverse or setting up scientific tools. Also triggers for "how do I use ToolUniverse", "what's the best way to access tools", "command line", "tu command", "coding API", "tu build".
What it loads into context
Frontmatter
221 tokensAlways available to the agent
SKILL.md
5,574 tokensWhen the skill is activated
Reference documents
18,541 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Agent Behavior
- Internal Notes (do not show)
- What is ToolUniverse?
- Step 1: Choose How to Use It
- Step 2: Install uv
- CLI Setup
- SDK Setup
- MCP Setup (Chat Mode)
- Step 3: API Keys
- Step 4: Test Together
- Step 5: Install Skills (Recommended for Chat Mode)
- What's Next? (Guided First Use)
- Write Agent Memory
- Team / Project-Level Setup
- Common Issues
- Quick Reference
Scripts executed separately
scripts/check_prerequisites.pyscripts/diagnose_setup.pyscripts/list_tool_categories.pyscripts/verify_installation.py
Files
../../../.claude-plugin/marketplace.jsonasset636 bytes../../.claude-plugin/plugin.jsonasset922 bytes../../hooks/hooks.jsonasset961 bytes../../hooks/hooks.jsonasset961 bytesAPI_KEYS_REFERENCE.mddoc14,075 bytesCLAUDE_DESKTOP.mddoc6,357 bytesEXAMPLES.mddoc9,435 bytesGITHUB_ISSUE.mddoc2,316 bytesINSTALL.mddoc3,666 bytesMCP_CONFIG.mddoc11,450 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILL.mdcore21,335 bytesSKILLS_CATALOG.mddoc6,062 bytesTRAE.mddoc7,345 bytesTROUBLESHOOTING.mddoc7,714 bytesreferences/mcp-configs.mddoc1,200 bytesscripts/check_prerequisites.pyruns5,477 bytesscripts/diagnose_setup.pyruns11,477 bytesscripts/list_tool_categories.pyruns2,754 bytesscripts/verify_installation.pyruns7,723 bytes
Read SKILL.md
# Setup ToolUniverse
Guide the user step-by-step through setting up ToolUniverse.
## Agent Behavior
- **Detect language** from user's first message. Respond in their language; keep commands/URLs in English.
- Go **one step at a time**. Ask before proceeding.
- Use **AskQuestion** for structured choices.
- **Explain briefly** in plain language. Celebrate small wins.
- When something goes wrong, help troubleshoot before moving on.
## Internal Notes (do not show)
View the complete source on GitHubtooluniverse
When your agent uses it
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, correlation, power), tooluniverse-image-analysis (microscopy, colony, fluorescence, dose-response, .tif — including ANOVA / Dunnett / power-analysis on image-derived measurements), tooluniverse-epigenomics (DNA methylation, CpG, m6A, MeRIP-seq, bisulfite, ChIP-seq, chromatin), tooluniverse-sequence-analysis (FASTQ, Trimmomatic, BWA, samtools, coverage), tooluniverse-variant-analysis (VCF, VAF, SNP, mutation), tooluniverse-phylogenetics (treeness, PhyKIT, parsimony), tooluniverse-single-cell (scRNA, h5ad, scanpy), tooluniverse-crispr-screen-analysis (MAGeCK, sgRNA), tooluniverse-proteomics-analysis (mass spec, TMT). Use for CSV/Excel/VCF/FASTA/h5ad and any biology/chemistry/medicine analysis question.
What it loads into context
Frontmatter
541 tokensAlways available to the agent
SKILL.md
12,350 tokensWhen the skill is activated
Reference documents
5,699 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- FIRST ACTION: Route to a Specialized Skill
- Critical Analysis Conventions
- Routing Table
- Tie-Breaking Rules
- When to Use General Strategies
- Problem-Solving Mode
- Quick Lookup Mode
- Routing Examples
- General Protocols (apply after routing)
Files
.env.templateasset499 bytesCHECKLIST.mddoc2,640 bytesREFERENCE.mddoc13,533 bytesSKILL.mdcore46,766 bytesSKILL.mdcore46,766 bytesSKILL.mdcore46,766 bytesSKILL.mdcore46,766 bytesSKILL.mdcore46,766 bytesreferences/general-strategies.mddoc5,414 bytes
Read SKILL.md
# 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:**
1. 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)
2. Find the matching keyword row in the Routing Table
3. Call `Skill(skill="<skill-name>")` immediately
4. 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.
View the complete source on GitHubtooluniverse-acmg-variant-classification
When your agent uses it
Systematic ACMG/AMP germline variant classification with all 28 criteria (PVS1, PS1-4, PM1-6, PP1-5, BA1, BS1-4, BP1-7) for clinical significance. Produces 5-tier verdict (Pathogenic / Likely Pathogenic / VUS / Likely Benign / Benign) with cited evidence per criterion. Use for variant interpretation, VUS resolution, and pathogenicity assessment. Combines ClinVar, gnomAD, computational predictors, and gene-mechanism context.
What it loads into context
Frontmatter
138 tokensAlways available to the agent
SKILL.md
4,713 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- ACMG Reasoning
- LOOK UP, DON'T GUESS
- When to Use
- Tool Parameter Reference
- Phase 0: Variant Validation and Normalization
- Phase 1: Population Frequency (BA1, BS1, BS2, PM2)
- Phase 2: Computational Predictions (PP3, BP4)
- Phase 3: Clinical Database Evidence (PS1, PM5, PP5, BP6)
- Phase 4: Functional Domain and Protein Analysis (PM1, PP2, BP1)
- Phase 5: Splice Impact Assessment (PVS1)
- Phase 6: Literature and Functional Evidence (PS3, BS3, PP1, PP4)
- Automated Cross-Check (InterVar)
- Classification Algorithm
- Output Format
- Common Patterns
Files
SKILL.mdcore17,768 bytesSKILL.mdcore17,768 bytesSKILL.mdcore17,768 bytesSKILL.mdcore17,768 bytesSKILL.mdcore17,768 bytesSKILL.mdcore17,768 bytesSKILL.mdcore17,768 bytesSKILL.mdcore17,768 bytesSKILL.mdcore17,768 bytes
Read SKILL.md
# ACMG/AMP Variant Classification
## ACMG Reasoning
Each criterion (PS, PM, PP for pathogenic; BS, BP for benign) contributes a weighted piece of evidence for or against pathogenicity. The classification is the COMBINATION of all activated criteria, not any single criterion. Do not overweight a single finding.
The hierarchy is: PVS1 (very strong) > PS (strong) > PM (moderate) > PP (supporting). On the benign side: BA1 (stand-alone) > BS (strong) > BP (supporting). A frameshift in a LOF-intolerant gene (PVS1) plus a ClinVar expert-panel pathogenic entry (PS1) is pathogenic. A single PP criterion alone is not. The combination rule is what matters.
Two common errors to avoid: (1) seeing a "Pathogenic" ClinVar entry and stopping — that is PP5 (supporting) unless it has expert-panel review, not automatic confirmation; (2) dismissing a variant because one predictor says "tolerated" — discordant predictors mean neither PP3 nor BP4 applies, which is neutral evidence, not benign evidence.
Always apply criteria conservatively. When evidence is ambiguous, leave the criterion unmet. Cite the source for every criterion you activate so clinicians can audit the reasoning.
**KEY PRINCIPLES**:
1. **Criteria-driven** — cite which criteria were activated and whyView the complete source on GitHubtooluniverse-admet-prediction
When your agent uses it
Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
4,419 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Before You Run
- COMPUTE, DON'T DESCRIBE
- KEY PRINCIPLES
- Evidence Grading
- Workflow: 5-Phase ADMET Profiling
- Completeness Checklist (MANDATORY before reporting)
Files
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# ADMET Prediction & Drug Candidate Profiling
**ADMET reasoning**: a drug fails if it can't be absorbed, distributes to wrong tissues, isn't metabolized safely, or isn't excreted. Evaluate each property independently — good absorption doesn't compensate for liver toxicity. The ADME properties determine whether a compound reaches its target at therapeutic concentrations; toxicity determines whether it's safe to do so. Prioritize experimental data (T2) over computational predictions (T3) — ADMETAI predictions are screening tools, not definitive verdicts. When a FAIL is flagged in any toxicity category (hERG, AMES, DILI), treat it as program-limiting until wet-lab data refutes it.
**LOOK UP DON'T GUESS**: never assume SMILES, CID, or experimental LD50 values — always call PubChem to resolve compound identity before any ADMETAI or PubChemTox call.
Comprehensive pharmacokinetic and toxicity profiling integrating AI-based ADMET predictions, rule-based drug-likeness filters, and experimental benchmarks from curated databases.
## When to Use This Skill
**Triggers**:
- "What are the ADMET properties of [compound]?"
- "Is [drug] likely to cross the blood-brain barrier?"
- "Predict the toxicity of this SMILES: ..."View the complete source on GitHubtooluniverse-adverse-event-detection
When your agent uses it
Detect and analyze adverse drug event signals using FDA FAERS reports, drug labels, and disproportionality statistics (PRR, ROR, IC). Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, regulatory submissions, and detecting rare AE signals not visible in clinical trials.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,987 tokensWhen the skill is activated
Reference documents
8,790 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Workflow Overview
- Phase Summaries
- Edge Cases
Files
PHASE_DETAILS.mddoc13,952 bytesQUICK_START.mddoc4,211 bytesREPORT_TEMPLATE.mddoc3,766 bytesSKILL.mdcore12,468 bytesSKILL.mdcore12,468 bytesTOOL_REFERENCE.mddoc8,669 bytes
Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Adverse Drug Event Signal Detection & Analysis
Automated pipeline for detecting, quantifying, and contextualizing adverse drug event signals using FAERS disproportionality analysis, FDA label mining, mechanism-based prediction, and literature evidence. Produces a quantitative Safety Signal Score (0-100) for regulatory and clinical decision-making.
**KEY PRINCIPLES**:
1. **Signal quantification first** - Every adverse event must have PRR/ROR/IC with confidence intervals
2. **Serious events priority** - Deaths, hospitalizations, life-threatening events always analyzed first
3. **Multi-source triangulation** - FAERS + FDA labels + OpenTargets + DrugBank + literature
4. **Context-aware assessment** - Distinguish drug-specific vs class-wide vs confounding signals
5. **Report-first approach** - Create report file FIRST, update progressively
6. **Evidence grading mandatory** - T1 (regulatory/boxed warning) through T4 (computational)View the complete source on GitHubtooluniverse-adverse-outcome-pathway
When your agent uses it
Map environmental and industrial chemicals to adverse outcome pathways (AOPs) — molecular initiating event to organ-level toxicity. Uses AOPWiki, GHS classification, IARC carcinogen status, and LD50 data. Use for environmental/industrial chemical risk assessment, regulatory-grade hazard characterization, and AOP stressor mapping. Distinct from drug-safety analysis (use tooluniverse-pharmacovigilance for drugs).
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
2,480 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Key Tools
- Workflow
- Tool Parameter Reference
- Common Patterns
- Reasoning Framework for Result Interpretation
- Fallback Chains
Files
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# Adverse Outcome Pathway & Regulatory Risk Assessment
Distinct from drug safety (see tooluniverse-toxicology): this skill targets **environmental and
industrial chemicals** where the focus is AOP stressor mapping, GHS classification, LD50 hazard
quantification, and IARC carcinogen status — not FAERS signals or FDA drug labels.
## When to Use
Apply when researcher asks about:
- "What AOPs are associated with [pesticide/solvent/industrial chemical]?"
- "What is the GHS hazard classification for [compound]?"
- "What is the LD50 for [compound]?"
- "Is [compound] a carcinogen (IARC classification)?"
- "Which genes does [chemical] interact with (CTD)?"View the complete source on GitHubtooluniverse-aging-senescence
When your agent uses it
Aging biology, cellular senescence, and longevity research. Covers senescence markers (p16/CDKN2A, SASP, SA-beta-gal), aging hallmarks, senolytic drug discovery (dasatinib+quercetin, fisetin, navitoclax), epigenetic clocks, telomere biology, and longevity GWAS. Use for senescence-pathway analysis, age-related disease genetics, senolytic-target discovery, and centenarian-genetics queries. Distinguishes correlative vs causal evidence (knockout, intervention).
What it loads into context
Frontmatter
133 tokensAlways available to the agent
SKILL.md
2,957 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Aging Research Reasoning
- LOOK UP, DON'T GUESS
- When to Use
- Workflow
- Phase 1: Hallmarks Classification
- Phase 2: Genetic Evidence
- Phase 3: Pathway Analysis
- Phase 4: Senolytic and Geroprotector Drug Discovery
- Phase 5: Literature and Clinical Context
- Phase 6: Report Structure
- Age-Dependent Expression Analysis
- Limitations
Files
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# Aging & Cellular Senescence Research
## Aging Research Reasoning
Before querying any tool, ask the central question: is this a cause or consequence of aging?
Senescence markers (SA-β-gal, p16/CDKN2A, SASP factors like IL-6 and IL-8) indicate that senescent cells are present. But their presence does not prove that senescence is driving the phenotype. Correlation is easy to establish. Causation requires an intervention. If senolytic drugs (dasatinib+quercetin, fisetin, navitoclax) clear senescent cells and the age-related phenotype improves, that is causal evidence. If clearing senescent cells has no effect, something else is driving the pathology.
Apply this reasoning when interpreting any gene or pathway query: classify it first by hallmark, then ask whether the evidence for its role is correlative (expression data, GWAS association) or causal (functional assay, genetic knockout, senolytic intervention).
Evidence grade the findings: T1 is human genetic evidence (GWAS, centenarian studies). T2 is model organism lifespan data. T3 is cell culture senescence data. T4 is computational prediction. Do not conflate T3 cell culture data with T1 human evidence — they are very different levels of confidence.
A final principle: cellular senescence is one hallmark of aging, not aging itself. Distinguish senescence from organismal aging, from age-related disease, and from progeria (accelerated aging syndromes). These require different tools and different interpretations.
View the complete source on GitHubtooluniverse-antibody-engineering
When your agent uses it
Therapeutic antibody engineering and optimization, lead-to-clinical-candidate. Covers sequence humanization (germline alignment, framework retention), affinity maturation, developability (aggregation, stability, PTMs), structure modeling (AlphaFold/PDB CDR analysis), immunogenicity prediction, and manufacturing feasibility. Use for biologic-drug optimization, mAb design review, biosimilar engineering, and clinical-precedent comparison.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
3,897 tokensWhen the skill is activated
Reference documents
27,709 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- When to Use
- Critical Workflow Requirements
- Phase 0: Tool Verification
- Workflow Overview
- Phase 1: Input Analysis & Characterization
- Phase 2: Humanization Strategy
- Phase 3: Structure Modeling & Analysis
- Phase 4: Affinity Optimization
- Phase 5: Developability Assessment
- Phase 6: Immunogenicity Prediction
- Phase 7: Manufacturing Feasibility
- Phase 8: Final Report & Recommendations
- Tool Reference
- Reference Files
Scripts executed separately
scripts/developability.py
Files
.env.templateasset499 bytesCHECKLISTS.mddoc3,811 bytesEXAMPLES.mddoc36,883 bytesMANUFACTURING.mddoc4,056 bytesQUICK_START.mddoc11,035 bytesREPORT_TEMPLATE.mddoc13,036 bytesSKILL.mdcore16,415 bytesSKILL.mdcore16,415 bytesWORKFLOW_DETAILS.mddoc17,367 bytesscripts/developability.pyruns6,039 bytes../tooluniverse-protein-design-campaign/SKILL.mddoc7,181 bytes../tooluniverse-protein-design-campaign/references/ph-selectivity.mddoc6,575 bytes
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# Antibody Engineering & Optimization
AI-guided antibody optimization pipeline from preclinical lead to clinical candidate. Covers sequence humanization, structure modeling, affinity optimization, developability assessment, immunogenicity prediction, and manufacturing feasibility.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create optimization report before analysis
2. **Evidence-graded humanization** - Score based on germline alignment and framework retention
3. **Developability-focused** - Assess aggregation, stability, PTMs, immunogenicity
4. **Structure-guided** - Use AlphaFold/PDB structures for CDR analysis
5. **Clinical precedent** - Reference approved antibodies for validation
6. **Quantitative scoring** - Developability score (0-100) combining multiple factors
7. **English-first queries** - Always use English terms in tool calls, even if user writes in another language. Respond in user's language
---View the complete source on GitHubtooluniverse-antigravity-plugin
When your agent uses it
Install, set up, verify, update, pin, uninstall, or troubleshoot the ToolUniverse plugin on Google Antigravity (AGY / Antigravity IDE / Antigravity 2.0). ALWAYS consult this skill for any of those — don't answer from memory, because the exact CLI name (agy), the "agy plugin install <path>" flow, the uvx tooluniverse MCP server, and the API-key env vars are easy to get wrong. Use it whenever someone wants to get ToolUniverse (or "the 1000+ scientific tools" / "the harvard tools") working on Antigravity, says the Antigravity plugin or its tools/skills won't load, hits a uvx or MCP-server startup error, asks how Antigravity updates it, wants to pin or remove it, or finds it running an old tool version. Not for the Codex plugin (use tooluniverse-codex-plugin) or Claude Code plugin (use tooluniverse-claude-code-plugin), for running research with the tools, or for authoring new tools or skills.
What it loads into context
Frontmatter
241 tokensAlways available to the agent
SKILL.md
1,024 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites (check once)
- Install
- Verify it worked
- What you get
- API keys (optional, but recommended)
- Update
- Troubleshooting
Files
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# Install the ToolUniverse Plugin for Google Antigravity (AGY)
One-step install on Google Antigravity (AGY / Antigravity IDE / Antigravity 2.0): an MCP server exposing 1000+ scientific tools plus 120+ research skills — all auto-configured.
## Prerequisites (check once)
```bash
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh
agy --version # Antigravity CLI; if missing: https://antigravity.google
```
## Install
In Antigravity, plugins can be installed using the `agy` CLI:View the complete source on GitHubtooluniverse-binder-discovery
When your agent uses it
Discover novel small-molecule binders for protein targets using structure-based and ligand-based screening. Covers druggability assessment, known-ligand mining (ChEMBL, BindingDB), similarity expansion, ADMET filtering, and synthesis feasibility. Use for hit identification, virtual screening, target-to-compounds workflows, and lead-finding before commit-to-medchem.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,855 tokensWhen the skill is activated
Reference documents
26,295 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Binding Site Reasoning (Start Here)
- Critical Workflow Requirements
- Workflow Overview
- Phase 0: Tool Verification
- Phase 1: Target Validation
- Phase 2: Known Ligand Mining
- Phase 3: Structure Analysis
- Phase 4: Compound Expansion
- Phase 5: ADMET Filtering
- Phase 6: Candidate Docking & Prioritization
- Phase 6.5: Literature Evidence
- Fallback Chains
- Programmatic Access (Beyond Tools)
- NVIDIA NIM Runtime Notes
- Reference Files
Files
CHECKLIST.mddoc8,321 bytesEXAMPLES.mddoc26,872 bytesREPORT_TEMPLATE.mddoc7,438 bytesSKILL.mdcore14,786 bytesSKILL.mdcore14,786 bytesSKILL.mdcore14,786 bytesSKILL.mdcore14,786 bytesTOOLS_REFERENCE.mddoc30,549 bytesWORKFLOW_DETAILS.mddoc20,025 bytes
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# Small Molecule Binder Discovery Strategy
Systematic discovery of novel small molecule binders using 60+ ToolUniverse tools across druggability assessment, known ligand mining, similarity expansion, ADMET filtering, and synthesis feasibility.
**LOOK UP DON'T GUESS** - Always retrieve actual data from tools before drawing conclusions. Do not assume druggability, binding sites, or compound properties based on target class alone.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Target validation FIRST** - Confirm druggability before compound searching
3. **Multi-strategy approach** - Combine structure-based and ligand-based methods
4. **ADMET-aware filtering** - Eliminate poor compounds early
5. **Evidence grading** - Grade candidates by supporting evidence
6. **Actionable output** - Provide prioritized candidates with rationale
7. **English-first queries** - Always use English terms in tool calls. Respond in the user's languageView the complete source on GitHubtooluniverse-bioinformatics-resource-discovery
When your agent uses it
Discover which bioinformatics software tool, biomedical REST API, R/Bioconductor package, CRAN package, or training material exists for a task, before writing analysis code or building a pipeline. Covers ELIXIR bio.tools (30,000+ software tools/databases by keyword, EDAM topic, or EDAM operation), SmartAPI (biomedical API registry, incl. NCATS Translator components), ELIXIR TeSS (bioinformatics training materials/events), Bioconductor (R packages for computational biology), and CRAN (general R packages). Use when someone asks 'is there an existing tool/package for X', 'what software does [analysis]', 'find an API for Y', or 'is there a tutorial on Z'.
What it loads into context
Frontmatter
171 tokensAlways available to the agent
SKILL.md
2,578 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- 1. bio.tools — Bioinformatics Software & Database Registry
- 2. SmartAPI — Biomedical API Registry
- 3. ELIXIR TeSS — Training Materials & Events
- 4. Bioconductor — R Packages for Computational Biology
- 5. CRAN — General R Package Registry
- Choosing Among the Five Resources
- Common Pitfalls
Files
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# Bioinformatics Resource Discovery
Answers "what already exists for this task" — software, APIs, R packages, or training material — **before** writing new analysis code, choosing an nf-core pipeline, or wrapping a new REST API by hand.
**LOOK UP, DON'T GUESS**: Never assert that a tool/package/API does or doesn't exist for a task from memory. These 5 registries are live and current — search them.
## When to Use This Skill
- "Is there existing software for [bioinformatics task]?" -> bio.tools
- "What biomedical APIs exist for [domain], and where's the base URL/spec?" -> SmartAPI
- "Is there a tutorial/course on [technique]?" -> ELIXIR TeSS
- "What R package does [analysis] in Bioconductor?" -> Bioconductor
- "What does CRAN package X do / what version / what does it depend on?" -> CRAN
View the complete source on GitHubtooluniverse-biomedical-fact-lookup
When your agent uses it
Answer biomedical FACTUAL / recall / multiple-choice questions by querying ToolUniverse database tools instead of answering from memory. Triggers on any 'which gene/drug/variant/disease/pathway/miRNA/TF...' lookup, any question phrased 'according to <database>' (DisGeNet, OMIM, MSigDB, miRDB, GTRD, MGI, Ensembl, ClinVar, ChEMBL, OpenTargets, Reactome, GtoPdb, UniProt...), and multiple-choice biology/medicine knowledge questions where one option must be verified against an authoritative source. NOT for analyzing user-supplied data files (CSV/VCF/h5ad → use the data-analysis router) and NOT for open-ended literature synthesis. Use whenever a single correct answer exists in a public biomedical database and could be looked up rather than guessed.
What it loads into context
Frontmatter
245 tokensAlways available to the agent
SKILL.md
7,357 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Six traps that produce a confidently wrong answer
- RULE ZERO: Look it up, never guess
- Multiple-choice procedure
- Routing table — question pattern → tool
- MSigDB set-name conventions (the most common LAB-Bench pattern)
- Gene–disease "in database X but NOT database Y" recipe
- Drug choice for a described patient (clinical vignette)
- Mouse-phenotype matching (MGI) — fallback only
- Computational procedures (when the answer is COMPUTED, not looked up)
- Interpretation
- Limitations (honest)
Files
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# Biomedical Fact Lookup (tool-grounded answering)
Factual biomedical questions — "which gene is in set X", "which gene is associated with disease Y according to DisGeNet", "which gene has a TF binding site per GTRD" — have an authoritative answer in a public database. Guessing from memory is unreliable (≈chance on niche annotations); the matching ToolUniverse tool returns the ground truth.
## Six traps that produce a confidently wrong answer
Each was observed producing a wrong answer on a real question. Check them before
answering; the detail for each is further down.
1. **"Highest p-value" in GWAS means most significant** — the *smallest* number.
Read literally it picks the study's weakest hit (`rs2476491` at 1e-06 instead
of `rs7775055-G` at 3e-174).
2. **A window of "N bp upstream plus M bp downstream" spans N+M+1 bases** — theView the complete source on GitHubtooluniverse-cancer-classification
When your agent uses it
Translate free-text tumor descriptions to OncoTree codes and resolve cancer subtypes/tissue hierarchy. Cross-references UMLS/NCI vocabularies. Use for standardizing cancer-type nomenclature in EHR free-text, building cohorts in OncoKB or GDC, mapping tumor-board notes to ontology codes, and ensuring consistent terminology across cancer-genomics pipelines.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
2,903 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Key Tools
- Workflow
- Tool Parameter Reference
- Common OncoTree Codes (verified working)
- Common Patterns
- Tumor Classification Reasoning (CRITICAL)
- Reasoning Framework for Result Interpretation
- Fallback Chains
Files
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# Cancer Classification via OncoTree
Standardize cancer type nomenclature using the OncoTree ontology. Resolves free-text tumor
descriptions to structured codes with UMLS/NCI cross-references, enabling downstream use in
OncoKB variant annotation and GDC cohort selection.
## When to Use
Apply when researcher asks about:
- "What is the OncoTree code for [tumor description]?"
- "Find all subtypes of [cancer type]"
- "What cancers originate in [tissue]?"
- "I need the tumor type code for OncoKB annotation"
- "What is the TCGA/COSMIC code for [cancer]?"View the complete source on GitHubtooluniverse-cancer-genomics-tcga
When your agent uses it
TCGA/GDC cancer genomics analysis — cohort construction, clinical metadata retrieval, somatic mutation frequencies, survival analysis, and multi-omics integration. Use for TCGA-BRCA-style cohort studies, mutation prevalence by cancer type, survival-by-mutation analysis, and pan-cancer driver discovery. Always cancer-type-specific (don't use pan-cancer counts without cohort context).
What it loads into context
Frontmatter
187 tokensAlways available to the agent
SKILL.md
4,248 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- NOT for (use other skills instead)
- Workflow Overview
- Key Identifiers
- Phase 1: Study Selection
- Phase 2: Clinical Data
- Phase 3: Somatic Mutations
- Phase 4: CNV Analysis (Progenetix)
- Phase 5: Survival Analysis
- Phase 6: Variant Interpretation (OncoKB)
- Tool Quick Reference
- Example Workflows
- Reasoning Framework
- Programmatic Access (Beyond Tools)
- Limitations
Files
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# Cancer Genomics / TCGA Analysis
**TCGA analysis starts with: what cancer type? what data type?** Build your cohort FIRST (GDC filters), then analyze. Don't query mutations without defining the cohort — pan-cancer counts from `GDC_get_mutation_frequency` are uninformative without cancer-type context. A mutation frequency of 10% in one cancer type may be 0.5% in another; always specify `project_id`. Survival analysis (Kaplan-Meier) is hypothesis-generating in retrospective TCGA data — always report sample size and p-value, and note that TCGA cohorts are not treatment-stratified.
**LOOK UP DON'T GUESS**: never assume TCGA project IDs, NCIt codes, or gene coordinates — use `GDC_list_projects` to confirm project IDs and `Progenetix_list_filtering_terms` for NCIt codes.
Systematic TCGA/GDC analysis: define cohorts, retrieve clinical data, profile somatic
mutations, query copy number variations, run survival analysis, and interpret variants
with OncoKB.
## When to Use
- "What is the mutation frequency of TP53 in TCGA-BRCA?"
- "Get survival data for TCGA-LUAD patients"View the complete source on GitHubtooluniverse-cancer-variant-interpretation
When your agent uses it
Clinical interpretation of somatic cancer mutations for precision oncology. Transforms a gene + variant + cancer-type input into an actionable report: clinical evidence tier (CIViC, OncoKB), therapeutic options (FDA-approved + investigational), resistance mechanisms, prognosis, and matching clinical trials. Use for tumor-board variant calls, somatic-mutation actionability assessment, and treatment selection. Always cancer-type-specific.
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
4,549 tokensWhen the skill is activated
Reference documents
15,363 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parsing
- Phase 0: Tool Parameter Verification (CRITICAL)
- Workflow Overview
- Clinical Reasoning Strategies
- Tool Reference (Verified Parameters)
- Fallback Strategy
Files
ANALYSIS_DETAILS.mddoc14,487 bytesEXAMPLES.mddoc7,095 bytesINPUT_PARSING.mddoc1,871 bytesQUICK_START.mddoc4,794 bytesREPORT_TEMPLATE.mddoc5,033 bytesSCORING_TABLES.mddoc4,400 bytesSKILL.mdcore18,499 bytesSKILL.mdcore18,499 bytesSKILL.mdcore18,499 bytesTOOLS_REFERENCE.mddoc19,228 bytes
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# Cancer Variant Interpretation for Precision Oncology
Comprehensive clinical interpretation of somatic mutations in cancer. Transforms a gene + variant input into an actionable precision oncology report covering clinical evidence, therapeutic options, resistance mechanisms, clinical trials, and prognostic implications.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Evidence-graded** - Every recommendation has an evidence tier (T1-T4)
3. **Actionable output** - Prioritized treatment options, not data dumps
4. **Clinical focus** - Answer "what should we treat with?" not "what databases exist?"
5. **Resistance-aware** - Always check for known resistance mechanisms
6. **Cancer-type specific** - Tailor all recommendations to the patient's cancer type when provided
7. **Source-referenced** - Every statement must cite the tool/database source
8. **English-first queries** - Always use English terms in tool calls (gene names, drug names, cancer types), even if the user writes in another language. Respond in the user's language
View the complete source on GitHubtooluniverse-cell-line-profiling
When your agent uses it
Cancer cell-line selection and profiling for experimental model choice. Cross-references DepMap, Cellosaurus, COSMIC, PharmacoDB to deliver identity verification, mutation/CNV profile, gene dependencies, drug sensitivities, and druggable targets. Use to answer 'which cell line should I use for studying gene X?' or 'is this cell line a good model for cancer Y?'. Outputs ranked recommendations with rationale, growth characteristics, and known pitfalls.
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
5,114 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Phase 0: Tool Parameter Reference (CRITICAL)
- Workflow Overview
- Phase 1: Cell Line Identification
- Phase 2: Molecular Profiling
- Phase 3: Gene Dependencies (CRISPR Screens)
- Phase 4: Drug Sensitivity
- Phase 5: Target Druggability and Recommendations
- Common Use Patterns
- Quick Reference: Common Cancer Cell Lines by Type
- Cross-Referencing Cell Line IDs
- Error Handling
- Completeness Checklist
Scripts executed separately
scripts/depmap_gene_dependency.py
Files
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# Cancer Cell Line Profiling and Selection
Comprehensive profiling of cancer cell lines for experimental model selection. Transforms a query (cancer type, gene, or cell line name) into an actionable report covering identity verification, molecular features, gene dependencies, drug sensitivities, and druggable targets.
**KEY PRINCIPLES**:
1. **Decision-first** - Answer "which cell line should I use?" not "here is all the data"
2. **Multi-source validation** - Cross-reference DepMap, Cellosaurus, COSMIC, PharmacoDB
3. **Actionable output** - Ranked cell line recommendations with rationale
4. **Practical focus** - Include availability, growth characteristics, common pitfalls
5. **Gene-aware** - When a gene of interest is given, prioritize lines with relevant mutations/dependencies
6. **Source-referenced** - Cite database sources for every claim
7. **English-first queries** - Always use English terms in tool calls, even if the user writes in another language
## LOOK UP, DON'T GUESSView the complete source on GitHubtooluniverse-chemical-compound-retrieval
When your agent uses it
Retrieve chemical compound data from PubChem and ChEMBL with disambiguation, cross-referencing, and stereochemistry handling. Use for resolving compound names to SMILES/InChI/CID/ChEMBL IDs (including OPSIN deterministic IUPAC-name-to-structure parsing), fetching molecular properties, distinguishing isomers/stereo forms, and cross-validating identity across databases. Always use English compound names; flags ambiguous queries (e.g., Vitamin D has multiple forms).
What it loads into context
Frontmatter
117 tokensAlways available to the agent
SKILL.md
2,218 tokensWhen the skill is activated
Reference documents
2,039 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning: Disambiguation
- Workflow
- Fallback Chains
- Evidence Grading
- SMILES Verification
- Identifier Resolution and Cross-Database Referencing
- Cheminformatics Calculations and Structure Visualization
- Tool Reference
Files
CHECKLIST.mddoc1,257 bytesSKILL.mdcore8,249 bytesSKILL.mdcore8,249 bytesSKILL.mdcore8,249 bytesSKILL.mdcore8,249 bytesexamples.mddoc6,111 bytes
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# Chemical Compound Information Retrieval
Retrieve comprehensive chemical compound data with proper disambiguation and cross-database validation.
**LOOK UP DON'T GUESS**: Never assume a CID, ChEMBL ID, or molecular property value. Always retrieve from PubChem/ChEMBL.
**English-first**: Always use English compound names in tool calls. Respond in user's language.
## Domain Reasoning: Disambiguation
"Aspirin" = one compound. "Vitamin D" = multiple forms (D2/D3/active metabolite). For generic class names (steroids, vitamins, acids), present candidates and confirm before proceeding.
---
View the complete source on GitHubtooluniverse-chemical-safety
When your agent uses it
Chemical safety and toxicology assessment integrating ADMET-AI predictions, CTD toxicogenomics, PubChemTox experimental data, GHS/IARC hazard classification, and exposure-context analysis. Use for chemical hazard identification, occupational/consumer-product toxicity, dose-response evaluation, and acute (LD50) vs chronic toxicity assessment. Distinguishes drug toxicity from environmental chemical toxicity.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
3,565 tokensWhen the skill is activated
Reference documents
6,105 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- COMPUTE, DON'T DESCRIBE
- KEY PRINCIPLES
- Evidence Grading System (MANDATORY)
- Core Strategy: 8 Research Phases
- Tool Summary by Phase
- Risk Classification Matrix
- Report Structure
- Mandatory Completeness Checklist
- Common Use Patterns
- Limitations
- Reference Files
- Summary
Files
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# Chemical Safety & Toxicology Assessment
**Toxicity assessment**: identify the chemical, check known hazards (GHS, IARC), then look for ADMET predictions. Dose makes the poison — always consider exposure level, as a compound that is toxic at high doses may be safe at relevant exposures. Distinguish between acute toxicity (LD50, GHS category) and chronic hazards (carcinogenicity, endocrine disruption) — they require different risk management approaches. Computational predictions (ADMETAI) are T3 evidence and must be anchored by experimental data from PubChemTox or FDA labels wherever available. When evidence conflicts between prediction and experiment, always defer to the experimental finding.
**LOOK UP DON'T GUESS**: never assume GHS categories, IARC classification, or CTD disease links — always call PubChemTox and CTD tools to retrieve current classifications before reporting.
Comprehensive chemical safety analysis integrating predictive AI models, curated toxicogenomics databases, regulatory safety data, and chemical-biological interaction networks.
## When to Use This Skill
**Triggers**:
- "Is this chemical toxic?" / "Assess the safety profile of [drug/chemical]"
- "What are the ADMET properties of [SMILES]?"
- "What genes does [chemical] interact with?" / "What diseases are linked to [chemical] exposure?"View the complete source on GitHubtooluniverse-chemical-sourcing
When your agent uses it
Find commercial sources for chemical compounds — PubChem/ChEMBL identity resolution then vendor catalog search across ZINC, Enamine, eMolecules, Mcule. Compares pricing, availability, and identifies purchasable analogs when an exact compound is not in stock. Use for chemical procurement, virtual library curation, and 'where can I buy X' questions for synthesis planning.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
3,104 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Databases
- Workflow Overview
- Phase Details
- Common Analysis Patterns
- Edge Cases & Fallbacks
- Interpretation Framework
- Limitations
Files
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# Chemical Compound Sourcing & Procurement
Pipeline for identifying, sourcing, and purchasing chemical compounds from commercial vendors. Resolves compound identity through PubChem/ChEMBL, searches multiple vendor databases (ZINC, Enamine, eMolecules, Mcule), compares pricing and availability, and identifies purchasable analogs when exact compounds are unavailable.
**Guiding principles**:
1. **Identity first** -- confirm the compound's structure (SMILES, InChI) before searching vendors; names can be ambiguous
2. **Multi-vendor comparison** -- always check multiple sources; pricing and stock vary significantly
3. **Analog fallback** -- if the exact compound is unavailable, search for close analogs
4. **Purity and quantity awareness** -- note catalog purity grades and minimum order quantities
5. **Structure over name** -- vendor searches by SMILES/InChI are more reliable than name searches
6. **English-first queries** -- use English compound names in tool calls
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.View the complete source on GitHubtooluniverse-claude-code-plugin
When your agent uses it
Install the ToolUniverse Claude Code plugin in one step — provides MCP server with 1000+ scientific tools, 120+ research skills, slash commands, hooks, and the research agent. Use for first-time plugin install, troubleshooting plugin not loading, verifying MCP server connection, listing API key requirements, or configuring auto-update.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
2,791 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites (check once)
- Install
- Verify it worked
- What you get
- API keys (optional, but recommended)
- Update
- Uninstall
- Alternative install paths
- Troubleshooting
- Automated diagnosis & repair
- For plugin maintainers
Scripts executed separately
../../../scripts/release-plugin.sh
Files
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Read SKILL.md
# Install the ToolUniverse Plugin for Claude Code
One-step install of the ToolUniverse plugin: MCP server with 1000+ tools, 120+ research skills, slash commands, and the research agent — all auto-configured.
## Prerequisites (check once)
```bash
uv --version # must exist; if not: curl -LsSf https://astral.sh/uv/install.sh | sh
claude --version # Claude Code CLI; if not: https://claude.com/claude-code
```
## Install
```bashView the complete source on GitHubtooluniverse-clinical-data-integration
When your agent uses it
End-to-end drug safety review integrating FDA labels, FAERS adverse event reports, PRR/ROR disproportionality, pharmacogenomic biomarkers, clinical trial data, and published literature. Use for regulatory drug safety reviews, comprehensive pharmacovigilance reports, label-vs-real-world AE comparison, and clinical decision support for drug safety.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
3,467 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Data Sources
- Workflow Overview
- Phase Details
- Common Analysis Patterns
- Edge Cases & Fallbacks
- Limitations
Files
SKILL.mdcore14,408 bytesSKILL.mdcore14,408 bytes
Read SKILL.md
# Clinical Data Integration for Drug Safety
End-to-end drug safety review pipeline that integrates FDA label information, FAERS spontaneous reports, disproportionality signal detection, pharmacogenomic biomarkers, clinical trial data, and published literature. Designed for regulatory assessments, pharmacovigilance, and clinical decision support.
**Guiding principles**:
1. **Label is ground truth** -- FDA-approved labeling is the authoritative starting point for known safety information
2. **Signals need context** -- a FAERS signal without label or literature corroboration is hypothesis-generating, not confirmatory
3. **Disproportionality is not causation** -- PRR/ROR measure reporting patterns, not causal relationships
4. **Pharmacogenomics narrows risk** -- PGx biomarkers can identify which patients face elevated risk
5. **Progressive reporting** -- create the report file early; update section by section
6. **English-first queries** -- use English drug names in all tool calls; respond in the user's language
Clinical data integration starts with data harmonization. Different hospitals code the same diagnosis differently (ICD-10 vs SNOMED). Before merging datasets, verify the coding system. Missing data is informative — a missing lab value may mean the test wasn't ordered (patient was stable) not that the result was normal.
View the complete source on GitHubtooluniverse-clinical-guidelines
When your agent uses it
Search and retrieve clinical practice guidelines from 12+ authoritative sources — NICE, WHO, NCCN, AHA, ADA, SIGN, USPSTF, IDSA, NIH consensus, ESMO/ESC/EASL European societies, and US specialty associations. Use for evidence-graded treatment recommendations, dosing protocols, screening guidance, and authoritative-source-prioritized clinical guidance (NICE/WHO ranked above society guidelines).
What it loads into context
Frontmatter
105 tokensAlways available to the agent
SKILL.md
4,725 tokensWhen the skill is activated
Reference documents
2,947 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Guideline Hierarchy
- COMPUTE, DON'T DESCRIBE
- Search Strategy
- Diagnostic Test Selection Reasoning
- Lab Test Interpretation Strategy
- Surgical Decision Making
- Applying Guidelines to Patients
- Tool Workflow
- Evidence Grading Quick Reference
- Fallback Strategy
- Synthesis Template
- Known Limitations
- Clinical MCQ Reasoning Framework
Files
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Read SKILL.md
# Clinical Guidelines Search & Retrieval
## Guideline Hierarchy
Not all guidelines carry equal weight. Evaluate sources in this order:
1. **NICE and WHO** — Evidence-graded, regularly updated, rigorous systematic review process. NICE guidelines include explicit recommendation strength (e.g., "offer" vs "consider").
2. **Society guidelines (AHA, ADA, NCCN, SIGN)** — Expert-consensus panels within a specialty. May lag behind the latest evidence by 1-3 years. Strong within their domain but narrower scope.
3. **Aggregator databases (TRIP, OpenAlex)** — Index guidelines from multiple societies. Good for breadth and discovery, but you must verify the original source.
4. **Literature databases (PubMed, EuropePMC)** — Return guideline-related publications, not curated guideline text. Useful as a fallback, not a primary source.
**Always check publication date.** A 2015 guideline may be superseded by a 2024 update. When presenting results, include the year prominently and note if newer guidance may exist.
---View the complete source on GitHubtooluniverse-clinical-risk-scoring
When your agent uses it
Compute and interpret validated bedside clinical risk scores and pretest probabilities for an INDIVIDUAL patient — pick the right score for the scenario, gather inputs, run the deterministic calculator tool, and read the result against an interpretation table. Covers CHA2DS2-VASc (AF stroke risk), HAS-BLED (bleeding on anticoagulation), CURB-65 (pneumonia severity / admit decision), qSOFA (sepsis screen), Child-Pugh + MELD-Na (cirrhosis severity / transplant priority), Wells DVT and Wells PE (VTE pretest probability), ASCVD (10-year cardiovascular risk / statin decision), and eGFR CKD-EPI (kidney function / drug dosing). Use when asked things like "stroke risk for this AF patient", "should this patient be anticoagulated", "pneumonia severity — admit or not?", "sepsis screen this patient", "DVT/PE pretest probability", "10-year cardiovascular risk", "cirrhosis severity / MELD score", or "eGFR / kidney function". Pairs CHA2DS2-VASc with HAS-BLED to weigh anticoagulation. NOT for polygenic/genetic risk (use tooluniverse-polygenic-risk-score), NOT for population-level epidemiology/incidence (use tooluniverse-epidemiological-analysis), and NOT for diagnostic test sensitivity/specificity/likelihood-ratio math (use tooluniverse-diagnostic-test-evaluation).
What it loads into context
Frontmatter
329 tokensAlways available to the agent
SKILL.md
3,689 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Step 1 — Map the scenario to the score(s)
- Step 2 — Gather the required inputs
- Step 3 — Compute
- Step 4 — Interpret (per-score tables)
- Worked example A — Atrial fibrillation, weigh anticoagulation (paired)
- Worked example B — Cirrhosis severity and transplant priority (paired)
- Completeness checklist
- LIMITATIONS
Files
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Read SKILL.md
# Clinical Risk Scoring
Turn a clinical scenario into the right validated risk score, compute it with a deterministic calculator tool, and interpret the number into a clinical action. All 10 backing tools are pure-compute (no network, no API key) and return `{status, data:{score, interpretation, components, ...}}`.
This skill is **decision-support only** — see LIMITATIONS. It does not replace clinical judgment.
## Step 1 — Map the scenario to the score(s)
| Clinical scenario | Score(s) | Tool(s) |
|---|---|---|
| Atrial fibrillation — stroke risk / anticoagulate? | CHA2DS2-VASc **and** HAS-BLED (pair) | `ClinicalCalc_CHA2DS2_VASc` + `ClinicalCalc_HAS_BLED` |
| Community-acquired pneumonia — severity / admit? | CURB-65 | `ClinicalCalc_CURB_65` |
| Suspected sepsis (infection + ? deterioration) | qSOFA | `ClinicalCalc_qSOFA` |
| Cirrhosis / chronic liver disease severity | Child-Pugh **and** MELD-Na (pair) | `ClinicalCalc_Child_Pugh` + `ClinicalCalc_MELD_Na` |View the complete source on GitHubtooluniverse-clinical-terminology-lookup
When your agent uses it
Fast autocomplete/normalization lookups against the NLM Clinical Table Search Service — resolve a partial or informal drug name to its RxTerms display name and RxCUI, autocomplete a health condition or problem-list entry to its formal name and ICD-10-CM/ICD-9-CM codes, normalize a disease mention to a UMLS CUI, look up an HCPCS Level II billing/DME code, autocomplete a pharmacogenomic star allele (e.g. CYP2D6, CYP2C19) to its nucleotide/protein change, or find a US healthcare provider/organization by name via the NPPES NPI registry. Also covers standardized coding-system lookups: search or look up ICD-10-CM diagnosis codes directly by name or code, resolve a drug name to its full RxNorm identity (RXCUI, related brand/generic products, NDC package-level status/properties), browse NCI Thesaurus cancer-concept hierarchy and its cross-vocabulary maps to MedDRA/SNOMED/GDC, and search/resolve LOINC lab-test and clinical-form codes. Also resolves UniProt's own disease and functional-keyword controlled vocabularies (DI-/KW- IDs) for protein-annotation contexts. Use when someone gives a free-text or informal clinical term ("high blood pressure", "metfor...", "Cleveland Clinic") and needs the standardized name/code before a downstream lookup, or explicitly asks to "autocomplete", "look up the RxCUI for", "find the ICD-10 code for", "look up this star allele", "find the NPI for", "what does ICD-10 code X mean", "look up the NDC for this drug package", "find the NCIt code for this cancer concept", "what does this cancer concept map to in MedDRA", or "find the LOINC code for this lab test".
What it loads into context
Frontmatter
410 tokensAlways available to the agent
SKILL.md
8,067 tokensWhen the skill is activated
Reference documents
8,166 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Tools
- Standardized Coding Systems: ICD-10-CM and RxNorm
- Cancer Ontology and Lab-Test Coding: NCI Thesaurus and LOINC
- Protein-Annotation Vocabularies: UniProt Disease and Keywords
- Disease Ontology and Genetic-Condition Terminology: Mondo, DO, MedGen
- Cross-Ontology Search, Text Annotation, and Specialized Terminologies
- Workflow
- Output
Files
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Read SKILL.md
# Clinical Terminology Lookup (NLM Clinical Tables, ICD, RxNorm)
A **fast, lightweight normalization utility** — not a research skill. Six
autocomplete-style tools from the NLM Clinical Table Search Service turn a
partial or informal clinical term into a standardized name/code, plus two
standardized-coding-system tool families (ICD-10-CM diagnosis codes, RxNorm
drug identity/NDC data) for when the input is already a known term/code and
you need its full standardized record rather than autocomplete matching.
Use this as a **supporting first step** before a deeper lookup in another
skill, not as a destination in itself.
**LOOK UP, DON'T GUESS**: RxCUIs, ICD-10-CM codes, UMLS CUIs, HCPCS codes,
and star-allele nucleotide changes are exact identifiers — never guess one
from memory or infer it from the term's spelling. Always resolve throughView the complete source on GitHubtooluniverse-clinical-trial-design
When your agent uses it
Strategic clinical trial design feasibility assessment. Analyzes 6 dimensions (endpoint, population, comparator, effect size, duration, regulatory pathway) using precedent trials and FDA guidance. Produces enrollment projections, endpoint recommendations, and approval-pathway analysis. Use for trial-protocol design, power/sample-size estimation, comparator selection, and FDA submission strategy. Driven by precedent-based reasoning rather than first-principles math.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
3,122 tokensWhen the skill is activated
Reference documents
30,417 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- Core Principles
- When to Use This Skill
- Core Strategy: 6 Research Paths
- Report Structure (14 Sections)
- Tool Reference by Research Path
- Quick Start Example
- Next Steps
- Integration with Other Skills
- Programmatic Access (Beyond Tools)
- Reference Files
- Version Information
Scripts executed separately
python_implementation.pytrial_pipeline.py
Files
.env.templateasset499 bytesBEST_PRACTICES.mddoc4,866 bytesEXAMPLES.mddoc59,702 bytesQUICK_START.mddoc7,732 bytesREPORT_TEMPLATE.mddoc17,798 bytesSKILL.mdcore13,438 bytesSKILL.mdcore13,438 bytesSKILL.mdcore13,438 bytesSTUDY_DESIGN_PROCEDURES.mddoc5,555 bytesWORKFLOW_DETAILS.mddoc10,204 bytespython_implementation.pyruns14,716 bytestrial_pipeline.pyruns14,716 bytes
Read SKILL.md
# Clinical Trial Design Feasibility Assessment
Systematically assess clinical trial feasibility by analyzing 6 research dimensions. Produces comprehensive feasibility reports with quantitative enrollment projections, endpoint recommendations, and regulatory pathway analysis.
**IMPORTANT**: Always use English terms in tool calls (drug names, disease names, biomarker names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.
## Reasoning Before Searching
Trial design starts with the question, not the methods. Answer these four questions before running any tools — they determine everything else:
1. **What is the primary endpoint?** Is it overall survival (gold standard but slow), PFS (faster but surrogate), ORR (single-arm friendly but not always accepted), or a biomarker (needs validation as surrogate first)? The endpoint determines FDA pathway, statistical design, and duration.
2. **Who is the population?** Broad unselected vs. biomarker-enriched. Enriched populations have higher response rates, allowing smaller trials — but require a validated companion diagnostic and reduce the eligible patient pool.
3. **What is the comparator?** Placebo (only if no standard of care exists), active control (requires non-inferiority or superiority framing), or single-arm with historical control (acceptable for rare diseases or breakthrough designations, but FDA scrutiny is high).
4. **Is the effect size realistic given the mechanism?** A 20% improvement in ORR over SOC requires ~100 patients per arm. A 50% improvement requires ~30. If the mechanism only justifies a 10% improvement, the trial may be underpowered regardless of design. Check precedent effect sizes in similar trials before committing to an endpoint.View the complete source on GitHubtooluniverse-clinical-trial-matching
When your agent uses it
AI-driven patient-to-trial matching for precision oncology and rare-disease care. Transforms a patient's molecular profile (mutations, biomarkers, expression) and clinical state into ranked clinical-trial recommendations with evidence tiers. Searches ClinicalTrials.gov, the EU CTIS register (European/EEA trials), AND the ISRCTN registry (UK/international) plus cross-references CIViC, OpenTargets, ChEMBL, and FDA labels. Use for matching patients to trials by genotype, biomarker-driven trial selection, trial-eligibility scoring, and finding trials across the US, Europe, and the UK.
What it loads into context
Frontmatter
146 tokensAlways available to the agent
SKILL.md
3,361 tokensWhen the skill is activated
Reference documents
13,151 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Molecular Matching Priority
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parsing
- Workflow Overview
- Critical Tool Parameters
- Scoring Summary
- Parallelization Strategy
- Error Handling
- Reference Files
Files
EXAMPLES.mddoc5,399 bytesMATCHING_ALGORITHMS.mddoc11,117 bytesQUICK_START.mddoc4,786 bytesREPORT_TEMPLATE.mddoc5,896 bytesSCORING_CRITERIA.mddoc4,615 bytesSKILL.mdcore12,962 bytesSKILL.mdcore12,962 bytesTOOLS_REFERENCE.mddoc7,457 bytesTRIAL_SEARCH_PATTERNS.mddoc11,387 bytes
Read SKILL.md
# Clinical Trial Matching for Precision Medicine
Transform patient molecular profiles and clinical characteristics into prioritized clinical trial recommendations. Searches ClinicalTrials.gov and cross-references with molecular databases (CIViC, OpenTargets, ChEMBL, FDA) to produce evidence-graded, scored trial matches.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Patient-centric** - Every recommendation considers the individual patient's profile
3. **Molecular-first matching** - Prioritize trials targeting patient's specific biomarkers
## Molecular Matching Priority
Match patients to trials by molecular profile FIRST (specific mutations), then by disease stage, then by prior treatments. A patient with EGFR L858R should match to EGFR-targeted trials regardless of other factors.
4. **Evidence-graded** - Every recommendation has an evidence tier (T1-T4)
5. **Quantitative scoring** - Trial Match Score (0-100) for every trialView the complete source on GitHubtooluniverse-clinical-trial-protocol
When your agent uses it
Generate a full clinical trial protocol document (ICH E6(R2)/FDA-style) for a drug or medical device, including background, objectives/endpoints, study design, population/eligibility, intervention details, assessment schedule, statistical analysis plan with sample-size justification, safety monitoring, and regulatory/ethical sections. Use when users say "generate a clinical trial protocol", "draft a protocol for [drug/device]", "write up the study design as a protocol document", "create an FDA submission protocol", "help me write section X of a trial protocol", or "calculate the sample size for this trial". Produces a stakeholder-ready Markdown deliverable, not a feasibility score.
What it loads into context
Frontmatter
157 tokensAlways available to the agent
SKILL.md
1,853 tokensWhen the skill is activated
Reference documents
2,473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- NOT for (use other skills instead)
- Workflow
- Reference Files
- Integration with Other Skills
Scripts executed separately
scripts/sample_size_calculator.py
Files
SKILL.mdcore8,031 bytesSKILL.mdcore8,031 bytesreferences/protocol_sections_guide.mddoc4,641 bytesreferences/regulatory_pathway_notes.mddoc3,112 bytesscripts/sample_size_calculator.pyruns11,093 bytestemplates/protocol_template.mddoc3,115 bytes
Read SKILL.md
# Clinical Trial Protocol Generation
Turns a trial concept into an actual protocol DOCUMENT, grounded in real precedent-trial and regulatory-database lookups plus a genuine statistical sample-size calculation.
**This skill is a downstream step, not a replacement for `tooluniverse-clinical-trial-design`.** That skill answers "is this trial feasible, and what should endpoint/population/comparator/effect-size/duration/regulatory-pathway be?" (a 0-100 feasibility score with evidence grading). This skill takes those six answers — freshly run or already available from a prior `tooluniverse-clinical-trial-design` report — and drafts the actual protocol sections around them. **Do not re-derive feasibility scoring, endpoint selection reasoning, or regulatory-pathway strategy here — call `tooluniverse-clinical-trial-design` for that and consume its output.**
## When to Use This Skill
- "Write me a clinical trial protocol for [drug/device]"
- "Generate an FDA submission protocol for our Phase 2 study"
- "Draft the statistical analysis plan / sample size section"
- "I have the feasibility assessment — now turn it into a protocol document"
- "What sample size do I need for a [proportion/continuous/survival] endpoint?"
View the complete source on GitHubtooluniverse-comparative-genomics
When your agent uses it
Cross-species gene comparison and ortholog analysis. Integrates Ensembl Compara orthologs, NCBI Gene, UniProt, OLS, Monarch, and OpenTargets to identify orthologs, paralogs, sequence conservation, functional conservation across species, and lineage-specific gene gains/losses. Use for phylogenetic gene tracing, model-organism mapping, and evolutionary-genomics queries.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
6,559 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Conservation Reasoning Framework
- Workflow Overview
- Phase 1: Gene Identification & Validation
- Phase 2: Ortholog Discovery
- Phase 3: Sequence Retrieval
- Phase 4: Functional Annotation Comparison
- Phase 5: Cross-Species Phenotype Bridging
- Phase 6: Gene Tree & Evolutionary Context
- Synthesis Questions
- Fallback Strategies
- Limitations
Scripts executed separately
scripts/dnds.py
Files
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# Comparative Genomics & Ortholog Analysis
Cross-species gene comparison, ortholog identification, sequence retrieval, and functional conservation analysis integrating Ensembl Compara, NCBI, UniProt, OLS, Monarch, and OpenTargets.
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
## When to Use This Skill
**Triggers**:
- "Find the mouse ortholog of [human gene]"View the complete source on GitHubtooluniverse-computational-biophysics
When your agent uses it
Solve quantitative problems in biophysics — pharmacokinetics (PK volume of distribution, clearance, half-life), epidemiology (R0, attack rate), toxicology (LD50, NOAEL), population genetics (Hardy-Weinberg, Fst), enzyme kinetics (Michaelis-Menten), thermodynamics. Use for first-principles quantitative biology calculations, dose calculations, exposure assessment, and biophysical-property estimation.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
4,543 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Recognize the Physical Process
- 2. Reasoning Patterns by Problem Type
- 3. When to Compute vs. Estimate vs. Look Up
- 4. Python Computation Templates
- 5. Multiple-Choice Strategy
- 6. Bundled Scripts
- 7. Combinatorics & Counting Problems
- 8. Common Pitfalls to Flag
Scripts executed separately
scripts/burn_fluids.pyscripts/env_risk_assessment.pyscripts/enzyme_kinetics.pyscripts/epidemiology.pyscripts/fluid_calculations.pyscripts/herd_immunity.pyscripts/iv_drip_rate.pyscripts/mc_analyzer.pyscripts/radioactive_decay.py
Files
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Read SKILL.md
# Computational Biophysics & Quantitative Biology Skill
## 1. Recognize the Physical Process
The single most important step: identify what physical process the problem describes. In quantitative biology, almost every problem maps to one of these:
- **Drug enters body → distributes → is eliminated**: pharmacokinetics. Key quantities: dose, bioavailability, volume of distribution, clearance, half-life. The body is a compartment model.
- **Radioactive tracer decays over time**: nuclear medicine. Same math as drug elimination (exponential decay) but the rate constant is a physical property of the isotope, not a patient variable.
- **Pathogen spreads through population**: epidemiology. R₀ determines whether an epidemic grows or dies. Herd immunity threshold = 1 - 1/R₀. Every epidemic model starts here.
- **Ligand binds receptor**: binding equilibrium. At low [ligand], binding is linear. At saturation, all sites occupied. Kd = concentration at half-maximal binding. This same curve describes enzyme kinetics, drug-receptor occupancy, and surface adsorption.
- **Contaminant enters environment**: dilution + persistence. Two questions: what is the concentration after mixing (conservation of mass), and how long does it persist (exponential decay with environmental half-life)?
- **Two populations differ genetically**: population genetics. Fst measures differentiation. HWE tests if mating is random. Gene flow opposes drift.
- **Neurons communicate in a network**: computational neuroscience. Integrate-and-fire models, synaptic dynamics, balanced excitation/inhibition. Mean firing rate depends on input current relative to threshold.
View the complete source on GitHubtooluniverse-connect
When your agent uses it
How to use ToolUniverse Connect (connect.aiscientist.tools), the hosted ToolUniverse service, and when to use it instead of ToolUniverse installed on this computer. Use when the user asks how to use ToolUniverse or ToolUniverse Connect, how to connect Claude, Claude Code or Codex, whether to use the hosted service or a local install, why a tool says a login or API key is missing and where to add it, how to share a model or script with colleagues, how to use a colleague's computer or a TU-SHARE code, how to publish a tool, or when ToolUniverse tools are missing, signed out or not connecting.
What it loads into context
Frontmatter
143 tokensAlways available to the agent
SKILL.md
2,030 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Find out which ToolUniverse this user has
- 2. Hosted or local: which one to suggest
- 3. Connect an AI app to ToolUniverse Connect
- 4. After connecting: what can this user use?
- 5. A tool says a login (API key) is missing or rejected
- 6. Sharing, using shared computers, publishing (Connect only)
- 7. When something goes wrong
Files
SKILL.mdcore8,365 bytes
Read SKILL.md
# ToolUniverse Connect: what each part is for and how to use it
The person you are helping is usually a scientist, not a programmer. Answer in plain words, one step at a time. When you can do a step yourself (run a command, call a tool), do it; ask the user only for what only they can do, such as signing in or clicking **Allow**.
**Never ask the user to paste a key, token or password into the chat, and never write one into a file or command yourself.**
## 1. Find out which ToolUniverse this user has
| What you see | The user is on |
|---|---|
| ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which still works) | **ToolUniverse Connect** (hosted) |
| A ToolUniverse server started on this computer (`uvx tooluniverse`, `tu serve`), the `tu` command, or Python `from tooluniverse import ToolUniverse` | **Local ToolUniverse** |
| No ToolUniverse tools at all | **Not connected yet** |
View the complete source on GitHubtooluniverse-crispr-screen-analysis
When your agent uses it
Analyze CRISPR-Cas9 genetic screens — MAGeCK gene-level scores, sgRNA count QC, replicate correlation, hit prioritization, and pathway GSEA on screen output. Use for genome-wide essentiality screens, synthetic-lethality discovery, dropout vs positive-selection screen analysis, target identification, and resistance-screen interpretation. Includes screen-QC and statistical thresholds.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
2,712 tokensWhen the skill is activated
Reference documents
16,188 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- RULE ZERO — Check for pre-computed results FIRST
- Overview
- Core Workflow
- ToolUniverse Tool Integration
- Quick Start
- Domain Reasoning: Hits Are Statistical, Not Biological
- Interpretation Framework
- References
- Analysis conventions
- See Also
Scripts executed separately
scripts/reference_gene_sets.py
Files
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# ToolUniverse CRISPR Screen Analysis
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
---
Comprehensive skill for analyzing CRISPR-Cas9 genetic screens to identify essential genes, synthetic lethal interactions, and therapeutic targets through robust statistical analysis and pathway enrichment.View the complete source on GitHubtooluniverse-custom-tool
When your agent uses it
Add custom local tools to ToolUniverse alongside the 1000+ built-in tools. Covers JSON-config tools (simplest, no code), Python class tools (REST/SOAP/GraphQL APIs, computational logic), and best-practices for return schemas. Use for wrapping new APIs, adding domain-specific computations, or contributing tools to the registry.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
2,258 tokensWhen the skill is activated
Reference documents
8,476 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Option A — Workspace tools (local use)
- Option B — Plugin package (shareable, pip-installable)
- Offline / pure-computation tools
Files
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# Adding Custom Tools to ToolUniverse
**When to create a custom tool:** Create one if you need to access an API that ToolUniverse
doesn't cover, or if you need a specialized data transformation that no existing tool provides.
Start with the JSON config approach (simplest — no Python needed); escalate to a Python class
only if you need custom response parsing or stateful logic.
Three ways to add tools — pick the one that fits your needs:
| Approach | When to use |
|---|---|
| **JSON config** | REST API with standard request/response — no coding needed |
| **Python class (workspace)** | Custom logic for local/private use only |
| **Plugin package** | Reusable tools you want to share or install via pip |View the complete source on GitHubtooluniverse-data-integration-analysis
When your agent uses it
Integrate computed statistical results (DEGs, GWAS hits, associations) with biological context from ToolUniverse databases (UniProt, GO, Reactome, ClinVar, OpenTargets). Use for adding gene function/pathway/disease annotations to a result list, building biological narrative around statistical findings, and going beyond p-values to mechanism.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,809 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Step 1: Statistical Results to Biological Questions
- Step 2: Multi-Database Evidence Integration
- Step 3: Causal Reasoning
- Step 4: Cross-Validation
- Step 5: Actionable Reporting
Files
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do -- execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Data Integration Analysis
Bridge the gap between statistical results and biological understanding. After any computational analysis produces significant findings, this skill teaches how to interpret them using ToolUniverse's biological knowledge tools -- the key advantage over platforms that only do data analysis.
**IMPORTANT**: Always use English terms in tool calls (gene names, pathway names, organism names), even if the user writes in another language. Respond in the user's language.
---
## When to Use This Skill
Apply when:View the complete source on GitHubtooluniverse-data-wrangling
When your agent uses it
Universal data access patterns for downloading and parsing scientific data when ToolUniverse tools don't cover the source, only return metadata, or you need bulk records. Use for VCF/h5ad/BAM/SDF/GCT parsing, multi-step API workflows (search to filter to download to parse), thousands of records at once, or sources with no dedicated tool. Write Python code via Bash for every step.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
4,626 tokensWhen the skill is activated
Reference documents
3,341 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Decision: Tool vs Code
- Section A: Format Cookbook
- Section B: API Patterns by Domain
- Section C: Restricted/Uncovered Data Sources
- Section D: Universal Patterns
Files
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# Data Wrangling: Universal Access Patterns
Reference for downloading and parsing scientific data from any source. Write and run Python code via Bash for every step.
## When to Use
- ToolUniverse tool returned metadata/search results but you need **raw or bulk data**
- Data is in a format tools don't parse (VCF, h5ad, BAM, SDF, GCT)
- You need a **multi-step API workflow** (search -> filter -> download -> parse)
- The data source has **no ToolUniverse tool** at all
- You need **thousands of records**, not the 10-100 a tool returns
## Decision: Tool vs Code
View the complete source on GitHubtooluniverse-database-logins
When your agent uses it
Help the user add the login (API key) that a ToolUniverse tool needs for a database such as NCBI/PubMed, OMIM, DisGeNET, UMLS or OpenAlex, without the key ever passing through the chat. Explains which login unlocks what, where to get it, where to paste it, and retries the tool. Use when a tool says a login, API key or token is missing or rejected, or the user asks "which logins do I need", "add my OMIM key", or "why does this database need a key".
What it loads into context
Frontmatter
131 tokensAlways available to the agent
SKILL.md
1,061 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Find out which login is needed
- 2. Get the key (the user does this)
- 3. Save it where ToolUniverse can use it
- 4. Check it works
Files
SKILL.mdcore4,315 bytes
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# Add a database login for ToolUniverse
Most ToolUniverse tools need no login. A few databases require the user's own free or licensed account. The person you are helping is usually a scientist, not a programmer: explain in plain words, one step at a time.
**Never ask the user to paste a key, token or password into the chat, and never put one in a file, command or message yourself.** Keys are typed only into the ToolUniverse website (hosted use) or the user's own local settings (local use).
## 1. Find out which login is needed
- If a tool failed, its error names the missing or rejected setting (for example `OMIM_API_KEY`). Use that exact name.
- If the user asks in general, ask what they want to do, and suggest only the logins that unlock it. Do not push them to collect every key.
Common ones:
| Login | Unlocks | Where to get it | Cost / wait |View the complete source on GitHubtooluniverse-dataset-discovery
When your agent uses it
Find and evaluate research datasets for any scientific question. Maps research questions to required study designs (longitudinal vs cross-sectional, observational vs experimental, single-cohort vs multi-cohort). Use when the user asks 'find data about X', 'where can I get data on Y', or needs a specific cohort/survey/repository. Covers GEO, ArrayExpress, dbGaP, NHANES, UK Biobank, ClinicalTrials.gov, GWAS Catalog, and 30+ scientific repositories.
What it loads into context
Frontmatter
119 tokensAlways available to the agent
SKILL.md
2,405 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Step 1: Understand What the Research Question Requires
- Step 2: Search Strategy
- Step 3: Evaluate Dataset Fitness
- Step 4: Download and Analyze
- Step 5: Report Honestly
- LOOK UP, DON'T GUESS
Files
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# Dataset Discovery
## When to Use
- User asks "find me data about X" or "where can I get data on Y"
- User wants to analyze a relationship between variables
- User needs specific study designs (longitudinal, cross-sectional, experimental)
- User asks about specific surveys or cohorts
## Step 1: Understand What the Research Question Requires
Before searching, determine the **minimum data requirements**:
**Study design needed:**
- "Does X predict CHANGES in Y over time?" → longitudinal (same people measured repeatedly). Cross-sectional data CANNOT answer this — don't settle for it.View the complete source on GitHubtooluniverse-diagnostic-test-evaluation
When your agent uses it
Diagnostic test / biomarker accuracy — sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden) for a continuous biomarker; and post-test probability via Bayes. Use when you have test results vs a gold standard (binary 2x2, or a continuous score + true labels) and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result. Emphasizes the prevalence-dependence of PPV/NPV.
What it loads into context
Frontmatter
141 tokensAlways available to the agent
SKILL.md
1,467 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Which case are you in?
- Step 1 — Fixed-cutoff metrics from a 2×2 table
- Step 2 — ROC / AUC / optimal cutoff for a continuous biomarker
- Step 3 — Post-test probability (Bayes)
- Gotchas (state these)
- Honest limitations
- Related skills
Scripts executed separately
scripts/roc_analysis.py
Files
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# Diagnostic Test / Biomarker Accuracy Evaluation
Judge how well a test or biomarker discriminates disease — at a fixed cutoff (2×2) or across all cutoffs (ROC) — and turn a result into a probability of disease.
## Which case are you in?
| You have… | Go to |
|---|---|
| A 2×2 table (TP/FP/TN/FN) at a fixed cutoff | **Step 1** (`Epidemiology_diagnostic`) |
| A **continuous** biomarker score + true labels | **Step 2** (ROC / AUC / Youden, Python) |
| A test's sens/spec + a patient's pre-test probability | **Step 3** (`Epidemiology_bayesian`) |
## Step 1 — Fixed-cutoff metrics from a 2×2 table
View the complete source on GitHubtooluniverse-disease-research
When your agent uses it
Generate comprehensive disease research reports covering genetics (causal genes, GWAS, OMIM), pathways (Reactome, KEGG), drugs (existing therapies, repurposing candidates), clinical trials, epidemiology (prevalence, incidence), and phenotypes (HPO). Use for full disease overviews, comprehensive disease characterization, and orphan/rare-disease profiling.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
2,492 tokensWhen the skill is activated
Reference documents
16,809 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- When to Use
- Core Workflow: Report-First Approach
- Disease Mechanism Reasoning
- 10 Research Dimensions
- Report Template
- Citation Format
- Progressive Update Pattern
- Evidence Grading & Interpretation
- Final Report Quality Checklist
- Expected Output Scale
- Cross-Skill References
- Reference Files
Scripts executed separately
../../../skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py
Files
EXAMPLES.mddoc20,233 bytesREPORT_TEMPLATE.mddoc5,291 bytesRESEARCH_PROTOCOL.mddoc5,298 bytesSKILL.mdcore10,491 bytesSKILL.mdcore10,491 bytesSKILL.mdcore10,491 bytesSKILL.mdcore10,491 bytesSKILL.mdcore10,491 bytesSKILL.mdcore10,491 bytesSKILL.mdcore10,491 bytesTOOLS_REFERENCE.mddoc24,351 bytestool_usage_details.mddoc6,085 bytes../../../skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.pyruns36,160 bytes
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# ToolUniverse Disease Research
Generate a comprehensive disease research report with full source citations. The report is created as a markdown file and progressively updated during research.
**IMPORTANT**: Always use English disease names and search terms in tool calls. Respond in the user's language.
---
## LOOK UP, DON'T GUESS
When asked about a disease, query Orphanet/OMIM/DisGeNET FIRST. Don't rely on memory for prevalence, genetics, or treatment — these change over time. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.
---
View the complete source on GitHubtooluniverse-dose-response
When your agent uses it
Dose-response / concentration-response curve fitting — IC50, EC50, Hill slope, Emax/Emin efficacy, and relative potency from paired concentration vs response data (enzyme/cell assays, drug screening, agonist/antagonist pharmacology). Fits the 4-parameter logistic (Hill sigmoidal) model. Use when you have concentrations + responses and need a potency value, to compare two compounds' potency, or to judge curve quality. NOT for image-derived dose-response (use tooluniverse-image-analysis) and NOT for survival/regression (use tooluniverse-statistical-modeling).
What it loads into context
Frontmatter
138 tokensAlways available to the agent
SKILL.md
1,628 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- Step 1 — Prepare the data (where results go wrong)
- Step 2 — Fit / get the potency
- Step 3 — Interpret the four parameters
- Step 4 — Quality gotchas (state these, don't hide them)
- Honest limitations
- Related skills
Scripts executed separately
scripts/fit_dose_response.py
Files
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# Dose-Response / Concentration-Response Analysis
Turn paired **concentration vs response** measurements into a potency (IC50/EC50), a Hill slope, an efficacy (Emax), and a quality judgment — and compare potency between compounds.
## When to use this
- Enzyme inhibition / activation assays, cell viability, reporter assays, radioligand binding, agonist/antagonist pharmacology.
- You have a list of concentrations and the response at each, and want IC50/EC50 + Hill slope.
- You want to say "compound A is N-fold more potent than B."
The model is the 4-parameter logistic (4PL) / Hill sigmoidal:
`f(x) = Emin + (Emax − Emin) / (1 + (EC50/x)^n)` — where `n` is the Hill slope.
## Step 1 — Prepare the data (where results go wrong)View the complete source on GitHubtooluniverse-drug-drug-interaction
When your agent uses it
Assess drug-drug interactions — CYP metabolic interactions (substrate/inhibitor/inducer), transporter (P-gp, BCRP, OATP) effects, pharmacodynamic synergy/antagonism, clinical significance scoring, and management recommendations. Use for polypharmacy review, prescribing decision support, and safety analysis when adding or switching drugs.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
4,713 tokensWhen the skill is activated
Reference documents
3,823 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOCAL PHARMACOLOGY REFERENCE (USE FIRST)
- LOOK UP, DON'T GUESS
- New Symptom After New Medication: First-Line Reasoning
- When to Use This Skill
- Clinical Reasoning Framework
- Critical Workflow Requirements
- Tool Workflow
- Output Report Structure
- Success Criteria
Scripts executed separately
ddi_pipeline.pyddi_working_example.pypython_implementation.pyscripts/pharmacology_ref.py
Files
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# Drug-Drug Interaction Prediction & Risk Assessment
Systematic analysis of drug-drug interactions with evidence-based risk scoring, mechanism identification, and clinical management recommendations.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create DDI_risk_report.md FIRST, then populate progressively
2. **Bidirectional analysis** - Always analyze A→B and B→A interactions (effects may differ)
3. **Evidence grading** - Grade all DDI claims by evidence quality (★★★ FDA label, ★★☆ clinical study, ★☆☆ theoretical)
4. **Risk scoring** - Multi-dimensional scoring (0-100) combining mechanism + severity + clinical evidence
5. **Patient safety focus** - Provide actionable clinical guidance, not just theoretical interactions
6. **Mandatory completeness** - All analysis sections must exist with explicit "No interaction found" when appropriate
---
View the complete source on GitHubtooluniverse-drug-mechanism-research
When your agent uses it
Trace drug mechanism of action — primary target → downstream signaling → pathway perturbation → tissue/organ effect → clinical outcome. Uses DrugBank, ChEMBL, KEGG, Reactome, STRING. Use for understanding how a drug works, identifying off-target effects, mechanism-based combination therapy design, and writing mechanism sections of reports.
What it loads into context
Frontmatter
175 tokensAlways available to the agent
SKILL.md
3,076 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Investigation Philosophy
- When to Use
- NOT for (use other skills)
- Step 1: Resolve the Drug
- Step 2: Identify the Primary Target
- Step 3: Assess Off-Target Effects
- Step 4: Map to Pathway Context
- Step 5: Get the Regulatory View (DailyMed)
- Step 6: Check Pharmacogenomics
- Step 7: Gather Literature Evidence
- Step 8: Integrate and Report
- Comparing Two Drugs
- Fallback Strategies
Files
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# Drug Mechanism of Action Investigation
## Investigation Philosophy
Drug mechanism research follows one core question chain:
**Target -> Downstream Effect -> Pathway -> Organ Effect -> Clinical Outcome**
Start with the drug's primary target. What receptor, enzyme, or transporter does it bind? Then trace forward: what does inhibiting/activating that target do immediately? What pathway is disrupted? What organ-level change results? What does the patient experience?
The LLM already knows drug pharmacology. This skill teaches HOW TO INVESTIGATE using available tools, not what mechanisms exist.
## When to Use
View the complete source on GitHubtooluniverse-drug-regulatory
When your agent uses it
Drug regulatory and approval research — FDA substance registry, ATC/EPC classification, EMA decisions, generic-drug status, FDA Orange Book exclusivity, NDA/BLA pathways. Use for jurisdiction-aware approval status (FDA vs EMA), generic vs brand availability, exclusivity expiry tracking, and regulatory pathway selection. Always specifies the market when reporting status.
What it loads into context
Frontmatter
182 tokensAlways available to the agent
SKILL.md
3,869 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- NOT for (use other skills instead)
- Workflow Overview
- Phase 1: Substance Identification (FDAGSRS)
- Phase 2: Drug Classification (RxClass)
- Phase 3: Approval & Generic Status (FDA Orange Book / Purple Book)
- Phase 4: Patent & Exclusivity
- Phase 5: Label Parsing (DailyMed)
- Phase 6: Clinical Trials
- Example Workflows
- Common Mistakes
- Reasoning Framework
Files
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# Drug Regulatory Research
**Regulatory status depends on jurisdiction.** FDA approval does not equal EMA approval — check the specific market the user is asking about. Generic availability depends on BOTH patent expiry AND regulatory approval — a patent may have expired but no ANDA may yet be filed or approved. Exclusivity codes (NCE, ODE, PED) can block generics even after patent expiry; always check `FDA_OrangeBook_get_exclusivity` before concluding a generic can enter. A 505(b)(2) NDA is not a generic — it requires its own clinical data and gets its own exclusivity period.
**LOOK UP DON'T GUESS**: never assume NDA numbers, exclusivity dates, or ATC codes — always call FDAGSRS, Orange Book, and RxClass tools to retrieve current data; regulatory status changes with new approvals and expirations.
Regulatory intelligence for drugs: identify FDA substances, classify drugs by therapeutic
category, check approval and generic status, retrieve label sections, and find clinical trials.
## When to Use
- "What is the FDA regulatory status of semaglutide?"
- "Is there a generic for Humira?"
- "What ATC class does metformin belong to?"View the complete source on GitHubtooluniverse-drug-repurposing
When your agent uses it
Identify drug repurposing candidates via target-based, compound-based, and disease-based strategies. Combines drug-target-disease network reasoning with mechanism rationale, clinical-trial precedent, and patent/regulatory feasibility. Use for hypothesis-generating repurposing for orphan diseases, finding existing drugs for new indications, and prioritizing candidates by evidence and feasibility.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
4,631 tokensWhen the skill is activated
Reference documents
20,568 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- Core Strategies
- Workflow Overview
- Quick Start
- Key ToolUniverse Tools
- Scoring & Decision Framework
- Best Practices
- Troubleshooting
- Reference Files
Files
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# Drug Repurposing with ToolUniverse
Systematically identify and evaluate drug repurposing candidates using multiple computational strategies.
**IMPORTANT**: Always use English terms in tool calls. Respond in the user's language.
---
## Reasoning Before Searching
Start by asking: WHY might this drug work for a new disease? Three strategies:
- **(a) Same target**: The drug's primary target is also involved in the new disease. This is the strongest hypothesis — use OpenTargets to check if the target has genetic evidence in both diseases before any other search.
- **(b) Off-target activity**: The drug has secondary targets or off-target effects that are relevant to the new disease. Check ChEMBL bioactivity data for all known targets of the drug, not just its primary one.View the complete source on GitHubtooluniverse-drug-research
When your agent uses it
Comprehensive drug profiling — mechanism, primary/secondary targets, drug interactions, clinical-trial status, adverse events (FAERS), pharmacogenomics, and approval history. Use for full drug investigation reports, 'tell me about drug X' queries, and assembling drug profiles for clinicians, researchers, or regulatory work.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
3,411 tokensWhen the skill is activated
Reference documents
17,645 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- Drug Mechanism Reasoning
- Workflow Overview
- Compound Disambiguation (Phase 1)
- Research Paths Summary
- FDA Label Core Fields
- Fallback Chains
- Quick Reference: Tools by Use Case
- Type Normalization
- Common Use Cases
- When NOT to Use This Skill
- Cross-Skill References
- Additional Resources
Scripts executed separately
../../../skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py
Files
CHECKLIST.mddoc8,067 bytesEXAMPLES.mddoc17,597 bytesREPORT_GUIDELINES.mddoc10,855 bytesREPORT_TEMPLATE.mddoc6,009 bytesSKILL.mdcore13,226 bytesSKILL.mdcore13,226 bytesSKILL.mdcore13,226 bytesTOOLS_REFERENCE.mddoc20,694 bytes../../../skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.pyruns36,294 bytes
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# Drug Research Strategy
Comprehensive drug investigation using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Compound disambiguation FIRST** - Resolve identifiers before research
3. **Citation requirements** - Every fact must have inline source attribution
4. **Evidence grading** - Grade claims by evidence strength (T1-T4)
5. **Mandatory completeness** - All sections must exist, even if "data unavailable"
6. **English-first queries** - Always use English drug/compound names in tool calls, even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language
---
View the complete source on GitHubtooluniverse-drug-synergy
When your agent uses it
Drug-combination synergy analysis — quantify whether two drugs together are synergistic, additive, or antagonistic using the standard reference models (Bliss independence, HSA / highest single agent, Loewe additivity, ZIP, and the Chou-Talalay Combination Index). Use when you have measured single-drug and combination effects (inhibition/viability) and need a synergy score. Explains which model to use, what data each one needs, and how to read the score. NOT for looking up pre-computed synergy in a database (use the SYNERGxDB tool / cell-line-profiling skill).
What it loads into context
Frontmatter
146 tokensAlways available to the agent
SKILL.md
1,513 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Step 0 — Pick the model by the data you have
- Step 1 — What each model's "additivity" means
- Step 2 — Run it
- Step 3 — Interpret the score
- Step 4 — Gotchas (state these)
- Honest limitations
- Related skills
Scripts executed separately
scripts/synergy_reference.py
Files
SKILL.mdcore5,924 bytesSKILL.mdcore5,924 bytesscripts/synergy_reference.pyruns2,271 bytes
Read SKILL.md
# Drug-Combination Synergy Analysis
Decide whether a two-drug combination does **more than expected** (synergy), exactly as expected (additivity), or **less** (antagonism) — and pick the right reference model for the data you have.
"Synergy" only means something relative to a **null model of additivity**, and the models define additivity differently — so the first decision is *which model*, driven by *what data you measured*.
## Step 0 — Pick the model by the data you have
| You measured… | Use model | Tool | Input |
|---|---|---|---|
| Single effects of A, B, and A+B at **one** dose pair | **Bliss** | `DrugSynergy_calculate_bliss` | `effect_a`, `effect_b`, `effect_combination` (each a fraction 0–1) |
| Effects of A, B, A+B across **several** dose points | **HSA** | `DrugSynergy_calculate_hsa` | `effects_a`, `effects_b`, `effects_combo` (arrays) |
| Single-agent **dose-response curves** + one combination point | **Loewe** | `DrugSynergy_calculate_loewe` | `doses_a_single`/`effects_a_single`, `doses_b_single`/`effects_b_single`, `dose_a_combo`, `dose_b_combo`, `effect_combo` |
| Single-agent dose-response + combo point, want Chou-Talalay CI | **Combination Index** | `DrugSynergy_calculate_ci` | same as Loewe + `assumption` |View the complete source on GitHubtooluniverse-drug-target-validation
When your agent uses it
Quantitative drug-target validation pipeline. Scores druggability, selectivity, safety profile, ADMET feasibility, and structural tractability with a composite Target Validation Score (0-100) and GO/NO-GO recommendation. Use for go/no-go decisions on a target before commit-to-medchem, target prioritization across a list, and target-deselection rationale.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
4,884 tokensWhen the skill is activated
Reference documents
8,191 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- COMPUTE, DON'T DESCRIBE
- Key Principles
- When to Use
- Input Parameters
- Reference Files
- Scoring Overview
- Pipeline Phases
- Report Output
Files
QUICK_START.mddoc3,984 bytesREPORT_TEMPLATE.mddoc7,662 bytesSCORING_CRITERIA.mddoc6,481 bytesSKILL.mdcore19,415 bytesSKILL.mdcore19,415 bytesTOOL_REFERENCE.mddoc11,113 bytes
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# Drug Target Validation Pipeline
Validate drug target hypotheses using multi-dimensional computational evidence before committing to wet-lab work. Produces a quantitative Target Validation Score (0-100) with priority tier classification and GO/NO-GO recommendation.
## Reasoning Before Searching
A valid drug target must pass 4 gates in order. Failing an early gate makes later gates irrelevant:
1. **Genetic evidence linking it to disease**: Does human genetic data (GWAS, rare variant studies, Mendelian genetics) support this target's role? Genetic evidence is the strongest predictor of clinical success. Use OpenTargets and GWAS catalog before anything else. If no genetic link exists, the hypothesis is speculative — document this clearly.
2. **Druggability**: Can a molecule reach and modulate the target? Check structure availability (PDB, AlphaFold), binding pocket prediction (ProteinsPlus), target class (kinase, GPCR, nuclear receptor = favorable; transcription factor, scaffold protein = difficult), and existing chemical probes.
3. **Safety — essentiality in normal tissue**: Is the target expressed in critical tissues (heart, liver, bone marrow)? Is knockout lethal in mice? High expression in essential tissue or lethality in mouse models is a strong safety red flag even before any clinical data.
4. **Competitive landscape**: Are other drugs already approved or in late-stage trials for this target? If so, the bar is differentiation, not first-in-class. Check ChEMBL, DrugBank, and ClinicalTrials.gov early.
Do not proceed to Phase 3 (Chemical Matter) before completing Phase 1 (Disease Association). Gate 1 failures should prompt a NO-GO or pivot recommendation.View the complete source on GitHubtooluniverse-ecology-biodiversity
When your agent uses it
Ecology, biodiversity, and conservation biology research — species identification (GBIF, NCBI Taxonomy), invasive species impact, ecosystem dynamics, conservation status (IUCN), niche ecology. Use for biodiversity questions, species comparison, invasion biology, conservation prioritization, and ecology-related literature search.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
3,079 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- Available Tools
- Navigating the GBIF taxonomic tree
- Additional Taxonomy & Biodiversity Sources
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
Files
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# Ecology & Biodiversity Research
## Reasoning Strategy
### 1. Species & Taxonomy Questions
When a question involves identifying or comparing species:
1. **LOOK UP DON'T GUESS** — Use `GBIF_search_species` to get taxonomy, `WoRMS_search_species` for marine organisms
2. If the question asks about invasive species impacts, consider: ecological niche overlap, reproductive rate, predator release, and ecosystem engineering effects
3. Use `PubMed_search_articles` or `EuropePMC_search_articles` to find studies on specific ecological impacts
### 2. Invasive Species Impact Assessment
**Reasoning framework** — when comparing invasive species impacts:
1. **Identify the ecosystem**: What habitat/biome is affected?
2. **Assess impact mechanisms**: Competition? Predation? Disease vector? Habitat modification? Hybridization?View the complete source on GitHubtooluniverse-electron-microscopy
When your agent uses it
Search and analyze electron microscopy data — cryo-EM density maps (EMDB), fitted atomic models (PDB), raw micrograph datasets (EMPIAR), and cryo-electron tomography volumes (CryoET Data Portal). Use for finding 3D structural data on a protein/complex, comparing experimental EM resolution to AlphaFold confidence, and accessing raw EM data for re-processing.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
3,831 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Databases
- Workflow Overview
- Phase Details
- Common Analysis Patterns
- Edge Cases & Fallbacks
- Related Resource: IDR (light microscopy & high-content screening)
- Limitations
Files
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# Electron Microscopy Structure Analysis
Pipeline for discovering and analyzing electron microscopy data across the full resolution spectrum: from 3D density maps (EMDB) to fitted atomic models (PDB), raw micrograph datasets (EMPIAR), and cryo-electron tomography volumes (CryoET Data Portal). Connects EM data to structural biology context via PDB and AlphaFold.
**Guiding principles**:
1. **Resolution awareness** -- always report and interpret map resolution; sub-4A enables atomic modeling, 4-8A enables domain fitting, >8A is shape-level
2. **Map before model** -- the density map is the primary experimental data; fitted models are interpretations
3. **Method matters** -- single particle analysis, tomography, 2D crystallography, and helical reconstruction have different strengths and limitations
4. **Raw data value** -- EMPIAR raw data enables reprocessing with newer algorithms; always note availability
5. **Cross-reference structures** -- connect EMDB maps to PDB entries and AlphaFold predictions for completeness
6. **English-first queries** -- use English terms in tool calls
EM resolution determines what you can see. TEM resolves individual protein complexes (~2nm). Cryo-EM achieves near-atomic resolution (<4Å) for large complexes. SEM shows surface topology. Choose the right EM modality for the question.
View the complete source on GitHubtooluniverse-enzyme-design
When your agent uses it
De novo enzyme design by catalytic-motif scaffolding — build a transition-state motif, generate scaffolds with AlphaProtein Novo, fold every mechanistic state, and triage with mechanism-inspired filters (catalytic geometry, multi-state H-bonds, multi-resequence backbone scoring). Use for designing enzymes for a named reaction, scoring or filtering existing designs, or deciding which designs to order.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
3,161 tokensWhen the skill is activated
Reference documents
4,967 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- What AP Novo is, exactly
- Workflow Overview
- Phase 1 — Define the reaction and build the motif
- Phase 2 — Validate the motif spec first
- Phase 3 — Generate scaffolds
- Phase 4 — Resequence, or don't
- Phase 5 — Fold every mechanistic state
- Phase 6 — Evaluate and filter
- Phase 7 — Backbone-ensemble scoring (MR / MPD)
- Phase 8 — Triage, novelty check, report
- Reference files
Files
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# De Novo Enzyme Designer
Design enzymes for a named reaction by scaffolding a catalytic motif, then triage the output with filters derived from the reaction's mechanism. Built around AlphaProtein Novo (AP Novo), whose own finding is that **the filters, not the generator, decided success**.
**KEY PRINCIPLES**:
1. **Motif-first** — the input motif dominates the outcome more than any model choice
2. **Mechanism-inspired metrics** — catalytic geometry beats generic confidence
3. **Multi-state** — fold every state along the mechanism, require agreement across them
4. **Backbone quality is an ensemble property** — score a backbone by how many resequences of it pass
5. **Report the funnel, not the survivors** — know which filter killed what
6. **In-silico pass is not activity** — grade designs, never promise them
7. **English-first queries** — always use English terms in tool calls
De novo enzyme design starts with chemistry, not protein. You must be able to draw the transition state or a covalent intermediate as 3D coordinates — catalytic side-chain atoms plus ligand — before any model is useful. Everything downstream is scaffolding that geometry and then asking whether a predictor still believes it.View the complete source on GitHubtooluniverse-enzyme-kinetics
When your agent uses it
Enzyme kinetics — Michaelis-Menten Km, Vmax, kcat (turnover), and kcat/Km (catalytic efficiency / specificity constant) from substrate-velocity data, plus inhibition-mechanism analysis (competitive / uncompetitive / non-competitive, Ki). Fits the MM equation by nonlinear regression (and reports Lineweaver-Burk for reference). Use when you have substrate concentrations and initial reaction velocities and need kinetic parameters or to classify an inhibitor. NOT for BRENDA database lookups of published constants (use the BRENDA tools).
What it loads into context
Frontmatter
134 tokensAlways available to the agent
SKILL.md
1,870 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- Step 1 — Prepare the data
- Step 2 — Fit Michaelis-Menten
- Step 3 — Interpret
- Step 4 — Inhibition mechanism
- Step 5 — Gotchas (state these)
- Honest limitations
- Related skills
Scripts executed separately
scripts/fit_michaelis_menten.py
Files
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# Enzyme Kinetics (Michaelis-Menten)
Turn **substrate concentration vs initial velocity** data into Km, Vmax, kcat, and catalytic efficiency — and classify an inhibitor's mechanism.
The Michaelis-Menten model: `v = Vmax·[S] / (Km + [S])`.
## When to use this
- You measured initial reaction rates at several substrate concentrations.
- You need Km (substrate affinity), Vmax, kcat (turnover number), or kcat/Km.
- You have ±inhibitor velocity data and want to classify the inhibition mode + Ki.
For *published* kinetic constants (someone else's Km/kcat), use the BRENDA tools or `SABIO_RK_search_reactions` instead — this skill is for analyzing **your own measured** data.
View the complete source on GitHubtooluniverse-epidemiological-analysis
When your agent uses it
End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step. Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
4,827 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Step 1: Formulate the Research Question (PECO Framework)
- Step 2: Find and Evaluate Data
- Step 3: Download and Prepare Data
- Step 4: Descriptive Statistics (Table 1)
- Step 5: Regression Analysis
- Step 6: Sensitivity Analyses
- Step 7: Biological Interpretation (ToolUniverse Advantage)
- Step 8: Visualization
- Step 9: Report Structure
- Completeness Checklist
Files
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# Epidemiological Data Analysis
Complete workflow for observational epidemiology — from research question to publication-ready report. Write and run Python code for every step. Never describe what you "would do" — do it.
## Step 1: Formulate the Research Question (PECO Framework)
Define **P**opulation, **E**xposure, **C**omparator, **O**utcome before touching data.
- **Population**: Who? (e.g., adults aged 20-79, cancer patients stage III+, ICU admissions)
- **Exposure**: What factor? (e.g., nutrient intake, drug treatment, gene mutation, environmental pollutant)
- **Comparator**: Vs. what? (e.g., lowest tertile, unexposed, wild-type, placebo)
- **Outcome**: What health event? (e.g., disease incidence, survival time, biomarker level, mortality)
**Study design check**: Does the question require temporality?View the complete source on GitHubtooluniverse-epigenomics
When your agent uses it
Genomics and epigenomics analysis: DNA methylation (CpG, 5mC, 5hmC, bisulfite, RRBS), m6A RNA modification (MeRIP-seq), ChIP-seq peaks, ATAC-seq accessibility, histone modifications, chromatin state, multi-omics integration. Combines pandas/scipy/pysam computation with ToolUniverse annotation tools. Use for genome-wide epigenomic statistics, methylation analysis, and chromatin-genome integration.
What it loads into context
Frontmatter
124 tokensAlways available to the agent
SKILL.md
3,110 tokensWhen the skill is activated
Reference documents
13,495 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- ⚠<U+FE0F> TOP-OF-MIND RULE: long-format methylation CSV — count ROWS, not unique positions
- RULE ZERO — Check for pre-computed results FIRST
- LOOK UP, DON'T GUESS
- When to Use
- Key Principles
- PRIMARY SCRIPT — methylation_density.py (use FIRST for CpG-density questions)
- Distinguish "rows" vs "unique sites" — methylation CSVs are usually long-format
- Workflow
- Common Patterns
- GTEx Tissue IDs
- Evidence Grading
- Limitations
- Reference Files
Scripts executed separately
scripts/methylation_density.py
Files
.env.templateasset499 bytesANALYSIS_PROCEDURES.mddoc5,078 bytesCODE_REFERENCE.mddoc33,821 bytesQUICK_START.mddoc8,934 bytesSKILL.mdcore12,206 bytesSKILL.mdcore12,206 bytesSKILL.mdcore12,206 bytesTOOLS_REFERENCE.mddoc3,733 bytesscripts/methylation_density.pyruns8,801 bytes
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# Genomics and Epigenomics Data Processing
## ⚠<U+FE0F> TOP-OF-MIND RULE: long-format methylation CSV — count ROWS, not unique positions
When the input is a long-format methylation CSV (one row per `(sample, CpG_position)`
e.g. columns `Pos, Chromosome, MethylationPercentage`), "how many sites are
removed when filtering" almost always means **rows removed**, NOT unique-position
removals. The two answers differ by a factor of ≈ `n_samples`.
| Question phrasing | What it means |
|---|---|
| "how many sites are removed when filtering …" | **rows removed** (= samples × positions failing the filter) |
| "how many unique CpG sites pass filter" | **unique positions** (dedupe by `Pos` then filter) |
View the complete source on GitHubtooluniverse-epigenomics-chromatin
When your agent uses it
Histone-modification ChIP-seq, ATAC-seq accessibility, chromatin state, and TF binding analysis from ENCODE, Roadmap Epigenomics, ChIP-Atlas. Use for chromatin-state-by-tissue queries, TF-binding-by-region, regulatory landscape mapping, and ENCODE-cCRE annotations. For DNA methylation use tooluniverse-epigenomics; for RNA-seq use tooluniverse-rnaseq-deseq2.
What it loads into context
Frontmatter
226 tokensAlways available to the agent
SKILL.md
3,722 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- NOT for (use other skills instead)
- Reasoning: Classify the Question First
- Reasoning: Histone Marks
- Reasoning: eQTL Interpretation
- Reasoning: Variant Regulatory Impact
- Phase 0: Disambiguation
- Phase 1: Histone Modification & ChIP-seq
- Phase 2: Chromatin Accessibility & Architecture
- Phase 3: Regulatory Element Identification
- Phase 4: eQTL Analysis
- Phase 5: Gene Expression Context
- Phase 6: Transcription Factor Binding
- Phase 7: Variant Regulatory Scoring
- Phase 8: Integration
- Fallback Strategies
Files
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# Epigenomics and Chromatin Accessibility Research
## NOT for (use other skills instead)
- Methylation array data processing (CpG beta values, differential methylation) -> Use `tooluniverse-epigenomics`
- RNA-seq differential expression -> Use `tooluniverse-rnaseq-deseq2`
- GWAS variant interpretation -> Use `tooluniverse-gwas-snp-interpretation`
- Variant functional annotation from VCF -> Use `tooluniverse-variant-analysis`
---
## Reasoning: Classify the Question First
Before calling any tool, identify which question type you're answering. Each maps to a different tool set.View the complete source on GitHubtooluniverse-expression-data-retrieval
When your agent uses it
Retrieve gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation and quality assessment. Use for finding RNA-seq/microarray datasets by organism/tissue/condition, comparing across studies (case-control, time-series, dose-response), and assessing dataset suitability before downloading. Always uses English search terms.
What it loads into context
Frontmatter
85 tokensAlways available to the agent
SKILL.md
1,902 tokensWhen the skill is activated
Reference documents
1,327 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- Workflow
- Phase 0: Clarification (When Needed)
- Phase 1: Query Disambiguation
- Phase 2: Data Retrieval (Internal)
- Phase 3: Report Dataset Profile
- Data Quality Tiers
- Reasoning Framework
- Error Handling
- Tool Reference
- Search Parameters
Files
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# Gene Expression & Omics Data Retrieval
Retrieve gene expression experiments and multi-omics datasets with disambiguation and quality assessment.
**IMPORTANT**: Always use English terms in tool calls. Respond in the user's language.
**LOOK UP DON'T GUESS**: Never assume which datasets exist or their accessions. Always search to confirm.
## Domain Reasoning
Before retrieving, determine: organism, tissue, experimental design (case-control/time-series/dose-response). These affect which database to search and how to interpret results. RNA-seq provides wider dynamic range; microarray has extensive legacy data. Prioritize experiments with >=3 biological replicates, complete annotations, and both raw+processed data.
## Workflow
View the complete source on GitHubtooluniverse-fastq-qc
When your agent uses it
FASTQ quality control and adapter/quality-trimming decisions with local NGS tools — run FastQC on raw reads, summarize a project with MultiQC, interpret per-base sequence quality, per-base N content, adapter content, overrepresented sequences, sequence duplication and GC content, and decide whether (and how) to trim with fastp / Cutadapt before downstream analysis. seqkit for read counts/stats/subsampling. Use when someone asks "run QC on my FASTQs", "are my reads good quality?", "do I need to trim adapters?", "interpret this FastQC report", "what does this WARN/FAIL mean", "why are overrepresented sequences flagged", "should I quality-trim before alignment", "make a MultiQC summary", or "clean up these reads with fastp". NOT for differential expression / DEG analysis (use tooluniverse-rnaseq-deseq2), NOT for read alignment, coverage, or variant calling (use tooluniverse-variant-analysis / tooluniverse-sequence-analysis). Honest: shells out to real local binaries; if a tool is missing it emits an install plan and stops rather than inventing QC numbers, and it never auto-trims or overwrites raw FASTQs.
What it loads into context
Frontmatter
267 tokensAlways available to the agent
SKILL.md
2,984 tokensWhen the skill is activated
Reference documents
3,067 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to use vs. not
- Essential inputs to confirm
- Preflight (do this first, every time)
- Tool roles
- Bundled orchestration script
- INTERPRETATION — FastQC module -> meaning -> action
- Workflow
- Limitations (honest)
- Completeness checklist
- References
Scripts executed separately
scripts/run_fastq_qc.py
Files
SKILL.mdcore11,991 bytesSKILL.mdcore11,991 bytesSKILL.mdcore11,991 bytesSKILL.mdcore11,991 bytesreferences/fastqc_interpretation.mddoc5,426 bytesreferences/tools_and_install.mddoc3,402 bytesreferences/trimming_decisions.mddoc2,769 bytesscripts/run_fastq_qc.pyruns9,751 bytes
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# FASTQ Quality Control & Trimming Decisions
Run quality control on raw sequencing reads, interpret the report, and make
an evidence-based decision about whether to trim — using real local
command-line tools (FastQC, MultiQC, fastp, Cutadapt, seqkit).
## Honesty contract (read first)
This skill drives **real binaries**. It must never fabricate QC numbers.
1. **Preflight before anything.** Check whether the required tools are on
PATH. If a required tool is missing, emit the install plan and STOP.
Do not estimate, guess, or describe hypothetical QC results.
2. **Never auto-trim.** Trimming is a *decision*. QC-only is the default.View the complete source on GitHubtooluniverse-functional-genomics-screens
When your agent uses it
Interpret hits from CRISPR-KO/CRISPRi/shRNA screens by integrating DepMap essentiality, gnomAD constraint scores, pathway context (Reactome, STRING), druggability (DGIdb), and clinical evidence (CIViC, COSMIC). Use for screen-hit prioritization, essentiality ranking, and turning a list of screen hits into a prioritized target shortlist.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
1,385 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Workflow
- Phase Details
- Edge Cases
- Limitations
Files
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# Functional Genomics Screen Interpretation
Pipeline for validating and prioritizing hits from genetic screens (CRISPR-KO, CRISPRi, shRNA) by integrating essentiality (DepMap), constraint (gnomAD), pathways (Reactome, STRING), druggability (DGIdb), and clinical evidence (CIViC, COSMIC).
**Guiding principles**:
1. **Hits are hypotheses** -- screen results contain false positives; validate through orthogonal evidence
2. **Selectivity matters** -- pan-essential genes are poor drug targets; context-specific essentiality is high-value
3. **Pathway over gene** -- enriched pathways are more robust than individual hits
4. **Druggability is practical** -- prioritize chemically modulable targets
5. **English-first queries** -- use English gene names in tool calls
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first.
View the complete source on GitHubtooluniverse-gene-disease-association
When your agent uses it
Gene-disease association analysis across DisGeNET, OpenTargets, Monarch, OMIM, GenCC, Orphanet, Gene2Phenotype (G2P). Cross-references multiple sources for evidence-graded association reports with concordance scoring (5/5 sources agree → strong, 1/5 → weak). Use for 'which diseases is gene X associated with' or 'which genes cause disease Y' queries with quantitative confidence, including clinical-panel-based curation (developmental disorders, cancer, cardiac, skeletal, eye, skin panels).
What it loads into context
Frontmatter
132 tokensAlways available to the agent
SKILL.md
3,733 tokensWhen the skill is activated
Reference documents
16,080 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- Core Principles
- Workflow Overview
- Phase 1: Gene/Disease Identification & ID Resolution
- Phase 2: DisGeNET Associations
- Phase 3: OpenTargets Associations
- Phase 4: Monarch Initiative Associations
- Phase 5: Mendelian Disease Evidence
- Phase 6: Variant-Disease Associations (Optional)
- Phase 7: Evidence Synthesis
- Common Patterns
- Troubleshooting
- Resources
Files
../tooluniverse-disease-research/SKILL.mddoc10,491 bytes../tooluniverse-drug-target-validation/SKILL.mddoc19,415 bytesSKILL.mdcore14,799 bytesSKILL.mdcore14,799 bytesSKILL.mdcore14,799 bytesSKILL.mdcore14,799 bytesSKILL.mdcore14,799 bytesSKILL.mdcore14,799 bytesSKILL.mdcore14,799 bytesSKILL.mdcore14,799 bytes../tooluniverse-rare-disease-diagnosis/SKILL.mddoc8,899 bytes../tooluniverse-variant-interpretation/SKILL.mddoc25,745 bytes
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# Gene-Disease Association Analysis
Systematically query and compare gene-disease associations across 6+ databases to produce a unified, evidence-graded report. Cross-references DisGeNET scores, OpenTargets evidence, Monarch Initiative cross-species data, OMIM Mendelian mappings, GenCC curated validity, and Orphanet rare disease links.
**IMPORTANT**: Always use English gene names and disease terms in tool calls. Respond in the user's language.
---
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
---
## Core PrinciplesView the complete source on GitHubtooluniverse-gene-enrichment
When your agent uses it
Gene-set enrichment analysis — GO (Biological Process, Molecular Function, Cellular Component), KEGG, Reactome pathway enrichment via clusterProfiler, gseapy, ORA, GSEA, plus g:Profiler for enrichment/ID conversion/ortholog mapping/SNP-to-gene annotation. Use for interpreting DEG lists, screen hit lists, or any gene-list-to-pathways query. Includes simplify-cutoff handling and union-vs-total denominator conventions for percent-DE questions.
What it loads into context
Frontmatter
113 tokensAlways available to the agent
SKILL.md
8,184 tokensWhen the skill is activated
Reference documents
29,986 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- RULE ZERO — Check for pre-computed results FIRST
- PRIMARY SCRIPTS — use these FIRST
- Domain Reasoning: Background Selection
- When to Use This Skill
- Input Parameters
- Core Principles
- Decision Tree: ORA vs GSEA
- Decision Tree: gseapy vs ToolUniverse Tools
- Quick Start Workflow
- Evidence Grading
- Supported Organisms
- Common Patterns
- Troubleshooting
- Tool Reference
- Sample-Level and Alternative Scoring Methods
- Detailed Documentation
- Analysis conventions
- Resources
Scripts executed separately
scripts/condition_enrichment_screen.pyscripts/enrichgo_runner.pyscripts/format_enrichment_output.pyscripts/gseapy_enrichment_runner.py../../../skills/tooluniverse-gene-enrichment/scripts/condition_enrichment_screen.py
Files
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Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Gene Enrichment and Pathway Analysis
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed enrichment files (CSV/TSV named `*enrich*`, `*go*`, `*kegg*`, `*reactome*`, `*ego*`, `*_simplified.csv`) → read directly
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running enrichment from raw DEG lists produces different numbers than the published answer due to subtle filter differences upstream, and is much slower.
View the complete source on GitHubtooluniverse-gene-liability
When your agent uses it
Evaluate the human safety liability of knocking down, knocking out, degrading, or pharmacologically inhibiting a gene. Use for gene safety scoring, on-target toxicity assessment, essentiality and genetic-constraint review, critical-organ expression analysis, or deciding whether a target needs partial, transient, or tissue-specific modulation.
What it loads into context
Frontmatter
73 tokensAlways available to the agent
SKILL.md
1,815 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Required Input
- Evidence Rules
- Workflow
- Required Output
Files
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# Gene Liability Evaluation
Assess whether reducing a human gene's function is likely to be unsafe. Resolve the
gene, gather independent human and model-system evidence, calculate a transparent
0–100 liability score, and recommend an appropriate modulation strategy.
Higher scores mean greater predicted liability from the stated intervention. This is
a safety score, not a target-efficacy or druggability score.
## Required Input
Accept a gene symbol, name, Ensembl ID, or UniProt accession. Also capture:
- intervention modality: knockout, knockdown, degrader, irreversible inhibitor,View the complete source on GitHubtooluniverse-gene-regulatory-networks
When your agent uses it
Gene regulatory network analysis — TF-target inference (JASPAR motifs, ChIP-seq), motif scanning, eQTL integration, perturbation evidence (knockout/overexpression). Use for 'which TF regulates gene X', 'which genes does TF Y target', regulatory pathway reconstruction. Distinguishes direct (binding) vs indirect (co-expression) regulatory evidence.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
3,047 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- COMPUTE, DON'T DESCRIBE
- Workflow
- Common Mistakes
- Common Use Patterns
- Evidence Grading
Files
SKILL.mdcore11,385 bytes
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# Gene Regulatory Network Analysis
**GRN inference starts with: which TF regulates which gene?** Direct evidence (ChIP-seq binding) is stronger than indirect (co-expression correlation). A TF binding near a gene doesn't prove regulation — check if expression changes when the TF is perturbed. JASPAR provides binding motifs but motif presence in a promoter is only computational evidence (T3); ENCODE ChIP-seq data that places the TF at the locus in the relevant cell type is stronger (T1). eQTLs from GTEx show which variants affect expression but don't identify the upstream regulator — combine with TF motif disruption analysis for mechanistic insight.
**LOOK UP DON'T GUESS**: never assume JASPAR matrix IDs, Enrichr library names, or GTEx tissue identifiers — always search JASPAR by TF name and verify library names before calling enrichr.
## When to Use
Activate this skill when the user asks about:
- Transcription factor (TF) binding sites, motifs, or target genes
- Gene regulatory networks or transcriptional regulation
- Chromatin state and histone modifications in regulatory context
- TF-target relationships and co-regulation
- eQTL effects on gene regulationView the complete source on GitHubtooluniverse-glycobiology
When your agent uses it
Glycobiology and glycomics research using GlyGen — glycan structure lookup by GlyTouCan accession, glycan search by mass/monosaccharide-count/type, glycoprotein glycosylation profiles by UniProt accession, glycosylation-site-level detail (position, flanking sequence, attached glycan, co-located SNVs). Use when someone asks about "what glycans are attached to protein X", "N-linked vs O-linked glycosylation sites", "glycan mass/composition search", "GlyTouCan accession lookup", "glycosylation site at position N", or "which proteins have reported glycosylation in human/mouse". NOT for protein sequence/structure retrieval alone (use tooluniverse-sequence-retrieval or tooluniverse-protein-structure-retrieval), NOT for general small-molecule chemistry (use tooluniverse-chemical-compound-retrieval), NOT for post-translational modifications other than glycosylation (use tooluniverse-protein-modification-analysis).
What it loads into context
Frontmatter
236 tokensAlways available to the agent
SKILL.md
1,868 tokensWhen the skill is activated
Reference documents
2,060 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Tool Chains for Common Questions
- Response Shape Notes (observed from live calls, not assumed from schema)
- Output
Files
SKILL.mdcore6,881 bytesreferences/glygen_tool_reference.mddoc6,416 bytes
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# Glycobiology and Glycomics (GlyGen)
Glycan structure and protein-glycosylation research using GlyGen's 5 ToolUniverse
tools: `GlyGen_get_glycan`, `GlyGen_search_glycans`, `GlyGen_get_glycoprotein`,
`GlyGen_search_glycoproteins`, `GlyGen_get_site`.
**LOOK UP, DON'T GUESS**: Glycan composition, mass, and site-level glycosylation
data are highly specific and not reliably recalled from memory — always resolve
through these tools rather than asserting a glycan's structure or a protein's
glycosylation status from general knowledge.
---
## When to Use This SkillView the complete source on GitHubtooluniverse-gpcr-structural-pharmacology
When your agent uses it
GPCR receptor pharmacology — agonist/antagonist/inverse-agonist/biased-agonist classification, GPCRdb structural data, receptor-ligand binding analysis, antibody-target interface (SAbDab). Use for GPCR drug discovery, biased-agonism analysis, receptor subtype selectivity questions, and orthosteric vs allosteric pocket characterization.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
3,556 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Tool Parameter Reference (CRITICAL)
- Workflow Overview
- Phase 1: GPCR Receptor Identification
- Phase 2: Ligand Landscape
- Phase 3: Structural Data
- Phase 4: Mutation Data
- Phase 5: Antibody Structure Retrieval
- Common Research Patterns
- Tool Combinations with Other Skills
- Fallback Chains
- Completeness Checklist
- Key References
Files
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# GPCR and Structural Pharmacology Research
**GPCR pharmacology**: agonist vs antagonist vs inverse agonist vs biased agonist — each has different clinical implications. Biased agonism (preferential G-protein vs β-arrestin signaling) can separate efficacy from side effects; for example, G-protein-biased opioid agonists aim to retain analgesia while reducing β-arrestin-mediated respiratory depression. Always classify retrieved ligands by their pharmacological type, not just their chemical structure. Receptor state (active vs inactive crystal structure) determines which ligands and mutations are interpretable — an inactive-state structure is appropriate for antagonist binding analysis, active-state for agonist-bound complexes. Generic GPCR numbering (Ballesteros-Weinstein) enables cross-receptor mutation comparison; always report positions in this system alongside sequence positions.
**LOOK UP DON'T GUESS**: never assume GPCRdb entry names (e.g., `adrb2_human`) or PDB IDs — always use `GPCRdb_list_proteins` to find the correct entry name and `GPCRdb_get_structures` to confirm available structures.
Research skill integrating GPCRdb (GPCR receptor biology), SAbDab (antibody structures), and PDBePISA (protein interface analysis) to support structural pharmacology, antibody engineering, and GPCR-targeted drug discovery.
**KEY PRINCIPLES**:
1. **Receptor-first** — Identify GPCR entry name before any GPCRdb queries
2. **Ligand classification** — Distinguish agonists, antagonists, partial agonists, biased agonists
3. **Structure-guided** — Pair GPCRdb mutation data with PDB structures via PDBePISA
4. **Antibody context** — Use SAbDab for therapeutic antibody structure retrieval and CDR analysis
5. **English-first queries** — Use standard receptor names (e.g., "beta-2 adrenergic receptor") in searches; convert to GPCRdb entry names for API callsView the complete source on GitHubtooluniverse-gwas-drug-discovery
When your agent uses it
Transform GWAS signals into drug targets and repurposing opportunities. Connects GWAS-significant loci to causal genes via fine-mapping/eQTL, then to druggable proteins via DGIdb/OpenTargets, then to existing drugs via ChEMBL. Use for GWAS-to-target hypothesis generation, druggable-fraction analysis of disease loci, and human-genetics-validated drug-repurposing prioritization.
What it loads into context
Frontmatter
104 tokensAlways available to the agent
SKILL.md
2,624 tokensWhen the skill is activated
Reference documents
12,126 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Reasoning Strategy
- Workflow Steps
- Quick Start
- All Tools by Category
- Best Practices
- Parameter Gotchas
- Interpretation: From GWAS Hit to Drug Target
- Troubleshooting
- Reference Files
Scripts executed separately
python_implementation.py
Files
EXAMPLES.mddoc2,119 bytesPROCEDURES.mddoc4,980 bytesQUICK_START.mddoc29,995 bytesREFERENCE.mddoc6,265 bytesREPORT_TEMPLATE.mddoc2,548 bytesSKILL.mdcore10,428 bytesSKILL.mdcore10,428 bytespython_implementation.pyruns14,635 bytes
Read SKILL.md
# GWAS-to-Drug Target Discovery
Transform genome-wide association studies (GWAS) into actionable drug targets and repurposing opportunities.
**IMPORTANT**: Always use English terms in tool calls. Respond in the user's language.
---
## Overview
This skill bridges genetic discoveries from GWAS with drug development by:
1. **Identifying genetic risk factors** - Finding genes associated with diseases
2. **Assessing druggability** - Evaluating which genes can be targeted by drugsView the complete source on GitHubtooluniverse-gwas-finemapping
When your agent uses it
Statistical fine-mapping of GWAS loci using credible sets (SuSiE, FINEMAP) and locus-to-gene scoring (Open Targets L2G). Identifies likely causal variants and target genes — distinct from positional 'nearest gene' which is often wrong. Use for prioritizing causal variants at GWAS hits, comparing fine-mapping methods, and converting lead SNPs to target genes.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
3,279 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- Overview
- Key Concepts
- Fine-Mapping Reasoning Framework (CRITICAL)
- Common Queries
- Tools Used
- Understanding Fine-Mapping Output
- Common Questions
- Limitations
- Best Practices
- References
- Related Skills
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesSKILL.mdcore13,408 bytespython_implementation.pyruns20,023 bytes
Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# GWAS Fine-Mapping & Causal Variant Prioritization
Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions.
## Overview
Genome-wide association studies (GWAS) identify genomic regions associated with traits, but linkage disequilibrium (LD) makes it difficult to pinpoint the causal variant. **Fine-mapping** uses Bayesian statistical methods to compute the posterior probability that each variant is causal, given the GWAS summary statistics.
**REASONING STRATEGY — Start Here**:
Fine-mapping asks: which variant at this locus is CAUSAL? Work through this chain:
1. **LD structure first** — variants in high LD (r² > 0.8) cannot be statistically distinguished from each other. Look up the LD block via Open Targets or the GWAS Catalog before assuming any single variant is the cause.View the complete source on GitHubtooluniverse-gwas-snp-interpretation
When your agent uses it
Interpret a single GWAS SNP across multiple databases — GWAS Catalog hits, LD/haplotype context, eQTL evidence, regulatory annotation, ClinVar pathogenicity, gnomAD frequency. Use for 'what does this SNP do', SNP-to-mechanism tracing, and resolving lead-SNP-vs-causal-variant ambiguity. Always considers LD structure before claiming a SNP is mechanistically responsible.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
2,135 tokensWhen the skill is activated
Reference documents
1,037 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- What It Does
- Workflow
- Data Sources
- Input Parameters
- Output Format
- Example Usage
- Tools Used
- Interpretation Guide
- Limitations
- Best Practices
- Technical Notes
- Related Skills
- References
- Version
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesFINAL_VERIFICATION.txtdoc4,872 bytesQUICK_START.mddoc3,719 bytesSKILL.mdcore8,260 bytesSKILL.mdcore8,260 bytesSKILL.mdcore8,260 bytesSKILL.mdcore8,260 bytespython_implementation.pyruns11,000 bytes
Read SKILL.md
# GWAS SNP Interpretation Skill
**SNP interpretation**: a GWAS hit is a REGION, not a single causal variant. The lead SNP may not be causal — it may be in LD with the causal variant. Always check LD structure and functional annotation before concluding a specific SNP is mechanistically responsible. Use `LDlink_get_proxies(variant="rs...", population="EUR")` to retrieve the high-R² LD proxies (needs a free LDLINK_TOKEN) — a proxy in a coding/regulatory region is a better mechanistic candidate than the lead SNP itself. Fine-mapping (SuSiE, FINEMAP credible sets) narrows the causal set but rarely identifies a single variant with certainty. L2G scores integrate eQTL, chromatin interaction, and distance data to predict the causal gene — a lead SNP mapping to gene A may actually regulate gene B 500 kb away via a distal enhancer.
**LOOK UP DON'T GUESS**: never assume a SNP's functional consequence, mapped gene, or population frequency — always call `gwas_get_snp_by_id` and `OpenTargets_get_variant_info` to retrieve current annotations.
## Overview
Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple sources to provide comprehensive clinical and biological context.
**Use Cases:**
- "Interpret rs7903146" (TCF7L2 diabetes variant)
- "What diseases is rs429358 associated with?" (APOE Alzheimer's variant)
- "Clinical significance of rs1801133" (MTHFR variant)View the complete source on GitHubtooluniverse-gwas-study-explorer
When your agent uses it
Compare GWAS studies, perform meta-analyses across cohorts, and assess signal replication. Uses GWAS Catalog metadata, study-level statistics, and cross-cohort comparison. Use for evaluating GWAS reproducibility for a trait, meta-analysis sample size and effect-size aggregation, and detecting study heterogeneity (population, design, ancestry).
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,464 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- COMPUTE, DON'T DESCRIBE
- Domain Reasoning: Comparing Studies for the Same Trait
- Use Cases
- Statistical Methods
- Study Quality Assessment
- Best Practices
- Limitations & Caveats
- Tools Used
- Glossary
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesSKILL.mdcore11,137 bytespython_implementation.pyruns24,517 bytes
Read SKILL.md
# GWAS Study Deep Dive & Meta-Analysis
**Compare GWAS studies, perform meta-analyses, and assess replication across cohorts**
---
## Overview
The GWAS Study Deep Dive & Meta-Analysis skill enables comprehensive comparison of genome-wide association studies (GWAS) for the same trait, meta-analysis of genetic loci across studies, and systematic assessment of replication and study quality. It integrates data from the NHGRI-EBI GWAS Catalog and Open Targets Genetics to provide a complete picture of the genetic architecture of complex traits.
### Key Capabilities
1. **Study Comparison**: Compare all GWAS studies for a trait, assessing sample sizes, ancestries, and platformsView the complete source on GitHubtooluniverse-gwas-trait-to-gene
When your agent uses it
Discover causal genes for diseases/traits from GWAS data using Open Targets L2G (locus-to-gene) scoring — integrates eQTL, chromatin interaction, and distance evidence. Use for trait-to-gene mapping, drug-target hypothesis generation from GWAS, and replacing the 'nearest gene' heuristic with multi-evidence L2G scores.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
3,220 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Use Cases
- Workflow
- Key Concepts
- Required ToolUniverse Tools
- Parameters
- Output Schema
- Example Results
- Best Practices
- Tool-Specific Notes (Updated)
- Programmatic Access (Beyond Tools)
- Limitations
- Related Skills
- Data Sources
- Citation
- Support
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesSKILL.mdcore12,112 bytesSKILL.mdcore12,112 bytesSKILL.mdcore12,112 bytesSKILL.mdcore12,112 bytesSKILL.mdcore12,112 bytespython_implementation.pyruns8,905 bytes
Read SKILL.md
# GWAS Trait-to-Gene Discovery
**Nearest gene is often wrong.** Use L2G (locus-to-gene) scores from Open Targets which integrate eQTL, chromatin interaction, and distance data. L2G > 0.5 is a strong prediction; positional mapping alone should not be used to claim a causal gene. A single GWAS study with p < 5e-8 is suggestive — replication across independent cohorts is required for high confidence. GWAS hits are associations in the studied population; effect sizes and even the implicated gene can differ across ancestries due to differing LD patterns. Treat gene lists from GWAS as ranked candidates for validation, not confirmed causal genes.
**LOOK UP DON'T GUESS**: never assume trait-to-gene mappings or L2G scores — always call `gwas_search_associations` and `OpenTargets_get_study_credible_sets` to retrieve current data; associations are updated as new GWAS are published.
**Discover genes associated with diseases and traits using genome-wide association studies (GWAS)**
## Overview
This skill enables systematic discovery of genes linked to diseases/traits by analyzing GWAS data from two major resources:
- **GWAS Catalog** (EBI/NHGRI): Curated catalog of published GWAS with >500,000 associations
- **Open Targets Genetics**: Fine-mapped GWAS signals with locus-to-gene (L2G) predictions
View the complete source on GitHubtooluniverse-hla-immunogenomics
When your agent uses it
HLA gene-family analysis and MHC-peptide binding for transplant compatibility, vaccine epitope coverage, and cancer immunotherapy. Uses IMGT (HLA polymorphism), IEDB (epitope-MHC binding), UniProt (annotation), DGIdb (druggability). Use for HLA typing/imputation review, vaccine HLA coverage, and immunotherapy prediction biomarkers (HLA-LOH, neoantigen presentation).
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
4,086 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Databases
- Workflow Overview
- Phase Details
- Edge Cases & Fallbacks
- Limitations
Files
SKILL.mdcore15,613 bytesSKILL.mdcore15,613 bytes
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# HLA & Immunogenomics Analysis
Pipeline for exploring HLA gene families, MHC-peptide binding, epitope associations, and their clinical implications in transplantation, vaccine development, and cancer immunotherapy. Bridges immunogenetic databases (IMGT, IEDB) with functional annotation (UniProt) and druggability data (DGIdb).
## Reasoning Strategy
HLA analysis is fundamentally about peptide presentation: the polymorphism of HLA molecules determines which peptides are displayed to T cells, which in turn governs disease susceptibility, transplant rejection, drug hypersensitivity, and vaccine immunogenicity. HLA type affects disease susceptibility for autoimmune conditions (HLA-B27 and ankylosing spondylitis), transplant rejection (HLA mismatch drives alloresponse), drug hypersensitivity (abacavir causes severe hypersensitivity reactions only in HLA-B*57:01 carriers), and vaccine design (epitopes must be presented by the recipient's HLA alleles to elicit a T-cell response). Class I and Class II HLA molecules have fundamentally different binding grooves, peptide lengths, and T-cell partners — never conflate them. The absence of an epitope from IEDB means it has not been tested, not that it cannot bind.
**LOOK UP DON'T GUESS**: Never assume an allele's binding properties or population frequency — query IEDB for experimental binding data and IMGT for allele annotation. Do not guess which HLA alleles are common in a population; look up published frequency data via PubMed.
**Guiding principles**:
1. **HLA nomenclature precision** -- HLA allele names follow strict conventions (e.g., HLA-A*02:01); get the resolution level right
2. **MHC class awareness** -- Class I (A, B, C) and Class II (DR, DQ, DP) have different binding properties and clinical roles
3. **Species context** -- most queries target human HLA, but MHC exists across vertebrates; confirm species earlyView the complete source on GitHubtooluniverse-image-analysis
When your agent uses it
Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing tabular outputs from CellProfiler/ImageJ, image-derived measurement statistics, and image-based assay quantification.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
3,208 tokensWhen the skill is activated
Reference documents
23,295 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- RULE ZERO — Check for pre-computed results FIRST
- CRITICAL — "Relative proportion of A to B" defaults to PERCENTAGE
- LOOK UP, DON'T GUESS
- When to Use
- Core Principles
- Required Packages
- Workflow Decision Tree
- Analysis Workflow
- Common Patterns
- Raw Image Processing
- R-to-Python Equivalents
- Answer Formatting
- Evidence Grading
- References
Scripts executed separately
scripts/batch_process.pyscripts/measure_fluorescence.pyscripts/segment_cells.py
Files
SKILL.mdcore12,610 bytesSKILL.mdcore12,610 bytesSKILL.mdcore12,610 bytesSKILL.mdcore12,610 bytesSKILL.mdcore12,610 bytesreferences/cell_counting.mddoc15,226 bytesreferences/fluorescence_analysis.mddoc14,692 bytesreferences/image_processing.mddoc11,945 bytesreferences/segmentation.mddoc17,933 bytesreferences/statistical_analysis.mddoc22,795 bytesreferences/troubleshooting.mddoc10,053 bytesscripts/batch_process.pyruns8,869 bytesscripts/measure_fluorescence.pyruns7,217 bytesscripts/segment_cells.pyruns7,588 bytes
Read SKILL.md
# Microscopy Image Analysis and Quantitative Imaging Data
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
---
## CRITICAL — "Relative proportion of A to B" defaults to PERCENTAGEView the complete source on GitHubtooluniverse-immune-repertoire-analysis
When your agent uses it
TCR/BCR repertoire analysis — V(D)J segment usage, CDR3 sequence diversity, clonality scoring, antigen specificity matching to IEDB, public-clone identification. Use for adaptive immune response characterization, post-treatment immune monitoring, antigen-specific clone tracking, and clonal-expansion analysis in immunotherapy or vaccination studies.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
2,952 tokensWhen the skill is activated
Reference documents
5,555 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- Overview
- Core Workflow
- ToolUniverse Tool Integration
- Quick Start
- Reasoning Framework for Result Interpretation
- References
- See Also
Files
ANALYSIS_DETAILS.mddoc15,485 bytesSKILL.mdcore12,226 bytesSKILL.mdcore12,226 bytesSKILL.mdcore12,226 bytesUSE_CASES.mddoc5,521 bytes
Read SKILL.md
# ToolUniverse Immune Repertoire Analysis
Comprehensive skill for analyzing T-cell receptor (TCR) and B-cell receptor (BCR) repertoire sequencing data to characterize adaptive immune responses, clonal expansion, and antigen specificity.
## Domain Reasoning
Repertoire diversity reflects immune history. High clonality — a few clones dominating — indicates antigen-driven expansion, as seen in active infection, tumor-infiltrating lymphocytes, or chronic stimulation. Low diversity points to immunodeficiency or treatment-induced lymphopenia. Always compare observed metrics against healthy donor reference distributions before drawing conclusions; a Shannon entropy of 7 is unremarkable in a healthy adult but alarming post-chemotherapy.
## LOOK UP DON'T GUESS
- Clonotype frequency thresholds, CDR3 length ranges, and convergence ratios: query IEDB or VDJdb; do not assume values from memory.
- Epitope specificities for expanded clones: search `iedb_search_tcell_assays` and `BVBRC_search_epitopes`; never infer antigen identity from CDR3 alone.
- V gene family usage biases in healthy donors: retrieve published reference data or query ImmPort; do not assume baseline distributions are uniform.
- Sequencing depth adequacy: compute rarefaction curves from the actual data; do not guess whether depth is sufficient.View the complete source on GitHubtooluniverse-immunology
When your agent uses it
Immunology research workflows: antibody-antigen interactions, T/B cell repertoire, MHC/HLA binding prediction, autoimmune disease genetics, vaccine epitope mapping. Uses IEDB, IMGT, SAbDab, UniProt. Use for adaptive immunity questions, immune response analysis, antibody/TCR/BCR characterization, immunogenicity prediction, and immune-pathway-to-disease mapping.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
3,621 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Reasoning Frameworks
- Tool Reference
- Parameter Gotchas
- Workflows
Files
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# Immunology Research Skill
**KEY PRINCIPLES**: Multi-layer evidence; source every claim; use immunology-specific databases first (IEDB, IMGT, SAbDab); always use English gene/protein names in tool calls.
---
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
**For MC about immune mechanisms**: Look up the specific pathway/receptor/cytokine before answering. Use `PubMed_search_articles` with the exact terms from the question. The answer is almost always in the first few search results.
**Specific LOOK UP targets** (never guess these):
- **Immune cell markers**: CD markers for cell subsets (e.g., Treg = CD4+CD25+FOXP3+, not just "CD4+"). Query UniProt or IEDB.
- **Cytokine functions**: IL-17 is pro-inflammatory (Th17), IL-10 is anti-inflammatory (Treg) — but context matters. Verify via KEGG pathway or PubMed.View the complete source on GitHubtooluniverse-immunotherapy-response-prediction
When your agent uses it
Predict patient response to immune checkpoint inhibitors (ICIs) by integrating tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression, HLA status, and immune-related gene expression. Outputs ICI Response Score with drug-specific recommendations and resistance-risk assessment. Use for melanoma/NSCLC/RCC immunotherapy decision support.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
2,750 tokensWhen the skill is activated
Reference documents
7,915 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parsing
- Workflow Overview
- Phase 1: Input Standardization & Cancer Context
- Phase 2: TMB Analysis
- Phase 3: Neoantigen Analysis
- Phase 4: MSI/MMR Status Assessment
- Phase 5: PD-L1 Expression Analysis
- Phase 6: Immune Microenvironment Profiling
- Phase 7: Mutation-Based Predictors
- Phase 8: Clinical Evidence & ICI Options
- Phase 9: Resistance Risk Assessment
- Phase 10: Multi-Biomarker Score Integration
- Phase 11: Clinical Recommendations
- Output Report
- Tool Parameter Reference
- Evidence Tiers
- References
Files
EXAMPLES.mddoc4,807 bytesINPUT_REFERENCE.mddoc1,585 bytesREPORT_TEMPLATE.mddoc5,146 bytesSCORING_TABLES.mddoc7,341 bytesSKILL.mdcore10,200 bytesSKILL.mdcore10,200 bytesTOOLS_REFERENCE.mddoc6,406 bytes
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# Immunotherapy Response Prediction
Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Transforms a patient tumor profile (cancer type + mutations + biomarkers) into a quantitative ICI Response Score with drug-specific recommendations, resistance risk assessment, and monitoring plan.
## Reasoning Before Searching
Not all tumors respond to checkpoint inhibitors. Reason through the biology before running tools:
- **TMB (tumor mutational burden)**: More somatic mutations produce more neoantigens, which are recognized by T cells. High TMB (>=10 mut/Mb, FDA-approved threshold for pembrolizumab) generally predicts better response — but this varies by cancer type (e.g., RCC responds despite low TMB).
- **MSI-H (microsatellite instability-high)**: Caused by defective DNA mismatch repair (MMR). MSI-H tumors have very high TMB and are pan-cancer approved for pembrolizumab. Check MLH1, MSH2, MSH6, PMS2 mutations.
- **PD-L1 expression**: The direct target of pembrolizumab/atezolizumab. High PD-L1 (TPS >=50% or CPS >=10 depending on cancer) predicts response in some cancers (NSCLC) but not all (melanoma, where TMB is more predictive).
- **Resistance factors** are equally important: STK11, KEAP1, JAK1/2 loss, B2M mutations can render an otherwise TMB-high tumor non-responsive.
Before calling any tool, determine which biomarkers are available for this patient and which are unknown. This determines which phases can be scored with data vs. must use cancer-type priors. Do not default to "moderate" for unknowns — flag them explicitly as missing.View the complete source on GitHubtooluniverse-infectious-disease
When your agent uses it
Rapid pathogen characterization and drug repurposing for outbreaks. Combines pathogen genomics (NCBI, BVBRC), host immune response (IEDB), drug-target databases (ChEMBL, DGIdb), and literature surveillance (PubMed/EuropePMC). Use for emerging-pathogen profiling, antiviral candidate identification, and outbreak intelligence reporting.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
2,916 tokensWhen the skill is activated
Reference documents
12,105 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Critical Workflow Requirements
- Phase 0: Tool Verification
- Workflow Overview
- Phase Summaries
- Evidence Grading
- Completeness Checklist
- Fallback Chains
- References
Files
CHECKLIST.mddoc6,652 bytesEXAMPLES.mddoc13,195 bytesSKILL.mdcore12,252 bytesSKILL.mdcore12,252 bytesTOOLS_REFERENCE.mddoc15,790 bytesphase_details.mddoc7,793 bytesreport_template.mddoc2,450 bytes
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Infectious Disease Outbreak Intelligence
Rapid response system for emerging pathogens using taxonomy analysis, target identification, structure prediction, and computational drug repurposing.
**KEY PRINCIPLES**:
1. **Speed is critical** - Optimize for rapid actionable intelligence
2. **Target essential proteins** - Focus on conserved, essential viral/bacterial proteins
3. **Leverage existing drugs** - Prioritize FDA-approved compounds for repurposing
4. **Structure-guided** - Use NvidiaNIM for rapid structure prediction and docking
5. **Evidence-graded** - Grade repurposing candidates by evidence strength
6. **Actionable output** - Prioritized drug candidates with rationaleView the complete source on GitHubtooluniverse-inorganic-physical-chemistry
When your agent uses it
Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, lattice parameters, thermodynamic properties, electronic structure. Use for unit cell volume calculations, coordination geometry, materials property estimation, and inorganic-mechanism reasoning. Complementary to tooluniverse-organic-chemistry.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
2,268 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- Available Tools
- LOOK UP DON'T GUESS
Scripts executed separately
scripts/equilibrium_solver.py../../../skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.py../../../skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.py../../../skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.py../../../skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.py../../../skills/tooluniverse-organic-chemistry/scripts/crystal_validator.py
Files
SKILL.mdcore8,350 bytesSKILL.mdcore8,350 bytesscripts/equilibrium_solver.pyruns8,373 bytes../../../skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.pyruns8,373 bytes../../../skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.pyruns8,373 bytes../../../skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.pyruns47,120 bytes../../../skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.pyruns47,120 bytes../../../skills/tooluniverse-organic-chemistry/scripts/crystal_validator.pyruns7,659 bytes
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# Inorganic & Physical Chemistry
## Reasoning Strategy
### 1. Crystal Structure Questions
**When given crystal structure data**, always COMPUTE don't guess:
1. **Calculate unit cell volume** for the crystal system:
- Cubic: V = a^3
- Tetragonal: V = a^2 * c
- Orthorhombic: V = a * b * c
- Monoclinic: V = a * b * c * sin(beta)
- Triclinic: V = a*b*c * sqrt(1 - cos^2(alpha) - cos^2(beta) - cos^2(gamma) + 2*cos(alpha)*cos(beta)*cos(gamma))
- Hexagonal: V = a^2 * c * sqrt(3)/2
View the complete source on GitHubtooluniverse-install-skills
When your agent uses it
Detect and auto-install missing ToolUniverse research skills. Checks common Claude Code/Cursor/Codex skill directories for the canary file, and installs any missing skills if none found. Use when the plugin's research skills aren't loading, when migrating between clients, or when verifying a skill installation.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
908 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Detection
- Installation
- Client Detection
- After Installation
Files
SKILL.mdcore3,142 bytesSKILL.mdcore3,142 bytesSKILL.mdcore3,142 bytesSKILL.mdcore3,142 bytes
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# ToolUniverse Install Skills
Checks whether the ToolUniverse specialized skills are installed and installs them automatically if not.
## Detection
Use the Shell tool to check for the canary file across all common client locations:
```bash
ls .cursor/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \
|| ls .agents/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \
|| ls .windsurf/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \
|| ls .gemini/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \
|| ls .claude/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \View the complete source on GitHubtooluniverse-kegg-disease-drug
When your agent uses it
KEGG-based disease-drug-variant network research. Connects diseases to causal genes, drugs to molecular targets, and variants to pathways using KEGG's editorially curated databases (KEGG Disease, Drug, Network, Variant, Pathway). Use for drug repurposing via shared pathways, mechanistic disease-gene-drug networks, and pathway-based target discovery. Distinguishes direct (binding) vs indirect (pathway co-membership) drug-target relationships.
What it loads into context
Frontmatter
174 tokensAlways available to the agent
SKILL.md
2,740 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- When to Use
- Tool Inventory (12 tools)
- Workflow
- Example Workflow: Find Drugs Targeting BRAF Variants in Cancer
- ID Conversion & Cross-Linking
- Integration with Other Skills
- Reasoning Framework for Result Interpretation
- Output
Files
SKILL.mdcore11,159 bytesSKILL.mdcore11,159 bytesSKILL.mdcore11,159 bytesSKILL.mdcore11,159 bytesSKILL.mdcore11,159 bytesSKILL.mdcore11,159 bytesSKILL.mdcore11,159 bytesSKILL.mdcore11,159 bytesSKILL.mdcore11,159 bytesSKILL.mdcore11,159 bytes
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# KEGG Disease-Drug-Variant Research
Systematic exploration of disease-drug-variant relationships using KEGG's curated databases.
## Reasoning Strategy
KEGG maps diseases to pathways and drugs to targets, but the real value is in the connections — which pathways link a disease gene to a drug target? This is a network question, not a simple lookup. A gene appearing in a KEGG disease entry has been editorially reviewed as mechanistically relevant; a drug entry with a confirmed target is more reliable than one inferred from pathway co-membership. When using KEGG for drug repurposing, always ask: is the drug-target relationship direct (the drug binds the gene product) or indirect (the drug affects a pathway that contains the gene)? Direct relationships are far stronger evidence. KEGG coverage is not exhaustive — absence from KEGG does not mean absence of biological involvement; complement with Reactome, WikiPathways, or CTD for broader coverage. ID namespace differences are a frequent source of errors: KEGG uses its own gene IDs (e.g., hsa:7157 for TP53), so always convert external IDs before querying KEGG-specific tools.
**LOOK UP DON'T GUESS**: Do not assume KEGG disease IDs, drug IDs, or gene IDs from memory — always search first with `KEGG_search_disease`, `KEGG_search_drug`, or `KEGG_convert_ids`. Do not assume which pathways link a disease gene to a drug; use `KEGG_link_entries` and `KEGG_get_network` to retrieve the actual connections.
## When to Use
- "What genes are associated with [disease] in KEGG?"
- "Find KEGG drugs targeting [gene/pathway]"View the complete source on GitHubtooluniverse-lab-data-standardization
When your agent uses it
Convert lab instrument output files (CSV, TSV, Excel, PDF — plate readers, spectrophotometers like NanoDrop, qPCR instruments, cell counters, chromatography systems) into a standardized Allotrope-Simple-Model-inspired JSON document plus a flattened 2D CSV, for LIMS/ELN upload or downstream analysis. Use when someone asks to "convert this instrument file", "standardize this lab data", "prepare this for LIMS upload", "what format is this plate reader/NanoDrop/qPCR output in", "turn this CSV into ASM/Allotrope format", or "separate the raw and calculated values in this instrument export". NOT for general data wrangling of arbitrary tabular data unrelated to lab instruments (use tooluniverse-data-wrangling), NOT for genomics/proteomics-specific file formats like FASTQ/VCF/mzML (use tooluniverse-fastq-qc, tooluniverse-variant-analysis, or tooluniverse-proteomics-analysis). Honest: never fabricates a measurement value; if a vendor's native parser (allotropy) is unavailable it says so and falls back to a generic tabular parser that clearly flags itself as non-certified.
What it loads into context
Frontmatter
267 tokensAlways available to the agent
SKILL.md
1,657 tokensWhen the skill is activated
Reference documents
1,942 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to use vs. not
- Two-tier conversion strategy
- Workflow
- Options reference (`convert_instrument_data.py`)
- References
Scripts executed separately
scripts/convert_instrument_data.pyscripts/flatten_to_csv.pyscripts/validate_output.py
Files
SKILL.mdcore6,812 bytesSKILL.mdcore6,812 bytesSKILL.mdcore6,812 bytesSKILL.mdcore6,812 bytesSKILL.mdcore6,812 bytesreferences/field_classification_guide.mddoc2,774 bytesreferences/schema_overview.mddoc2,695 bytesreferences/supported_instruments.mddoc2,555 bytesscripts/convert_instrument_data.pyruns14,410 bytesscripts/flatten_to_csv.pyruns1,703 bytesscripts/validate_output.pyruns4,800 bytes
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# Lab Instrument Data Standardization
Convert raw instrument output into a standardized, structured format —
distinguishing raw measurements from calculated/derived values with
explicit traceability — for LIMS/ELN upload, data lakes, or downstream
analysis.
## Honesty contract (read first)
1. **Never fabricate a value.** Every number in the output JSON/CSV must
come directly from the source file. If a value can't be found or
confidently parsed, it is omitted — never guessed.
2. **Never claim certified ASM output you didn't produce.** Tier 1 (native
`allotropy` parsing) produces genuine Allotrope Simple Model documents.View the complete source on GitHubtooluniverse-lab-protocols
When your agent uses it
Find and inspect published, peer-reviewed or community lab protocols from protocols.io — search by technique/keyword, pull a protocol's full description and materials list, or get its ordered step-by-step instructions. Use when someone asks to "find a protocol for X", "how do others do [technique]", "get the steps for protocol [ID/DOI]", or "check the materials list for this method" before designing or running a wet-lab experiment. NOT for designing a novel cloning/assembly strategy from scratch (use tooluniverse-molecular-cloning), NOT for primer design (use tooluniverse-primer-design). Honest: protocols.io requires an OAuth Bearer token (PROTOCOLS_IO_API_KEY) for every call, including public reads — without it these tools are blocked, not degraded.
What it loads into context
Frontmatter
183 tokensAlways available to the agent
SKILL.md
1,090 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to Use This Skill
- Workflow
- Notes
Files
SKILL.mdcore4,748 bytes
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# Lab Protocol Discovery (protocols.io)
Find a published, step-by-step lab protocol before designing your own from scratch, or pull the exact steps/materials for a protocol someone already pointed you to.
## Honesty contract (read first)
[Credential-related source omitted]
2. **Never fabricate a protocol.** If the key is missing or a lookup fails, say so — do not describe a plausible-sounding protocol from memory and present it as if it came from protocols.io.
3. **Tokens expire (~1 year).** A previously-working key that starts failing with an auth error may simply need refreshing, not re-registering from scratch.
## When to Use This Skill
Apply when users:
- Want to find a published protocol for a specific technique before running it themselves (e.g., "is there a protocols.io method for lentiviral titering?")View the complete source on GitHubtooluniverse-lipidomics
When your agent uses it
Lipid analysis and lipid-disease associations using LIPID MAPS classification, HMDB metabolite data, KEGG/Reactome lipid pathways (sphingolipid, eicosanoid, steroid, fatty acid), and PubChem chemical info. Use for lipid identification, lipid metabolism pathway mapping, and lipid-associated disease analysis (cardiovascular, diabetes, NAFLD).
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
3,119 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- When to Use
- Core Tools
- Workflow
- Limitations
Files
SKILL.mdcore11,965 bytesSKILL.mdcore11,965 bytesSKILL.mdcore11,965 bytesSKILL.mdcore11,965 bytes
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# Lipidomics Analysis
Integrated pipeline for lipid identification, classification, pathway mapping, and disease association analysis. Distinct from general metabolomics because lipids have unique classification systems (LIPID MAPS), specialized pathways (sphingolipid, eicosanoid, steroid), and disease associations (cardiovascular, neurodegeneration, metabolic syndrome).
## Reasoning Strategy
Lipid identification starts with mass spectrometry: the lipid class is determined by the head group fragment mass (e.g., m/z 184 for phosphocholine in positive mode), total chain length and saturation from the precursor exact mass, and individual fatty acid chains from neutral loss or product ion scans. LIPID MAPS classification organizes lipids by chemical structure into 8 categories — knowing the category immediately tells you the likely biological context (sphingolipids → apoptosis/neurodegeneration; glycerophospholipids → membrane remodeling; eicosanoids → inflammation). Structural specificity matters biologically: Cer(d18:1/16:0) and Cer(d18:1/24:1) have different membrane properties and disease associations despite being the same lipid class. Always map changed lipids back to metabolic pathways because lipids are intermediates — an elevated ceramide could mean increased synthesis (CERS activity up), decreased degradation (ASAH1 down), or shunting from sphingomyelin (SMPD1 up).
**LOOK UP DON'T GUESS**: Do not assume a lipid's LIPID MAPS ID, exact mass, or pathway membership — query `LipidMaps_search_by_name` first. Do not guess which diseases are associated with a lipid class; retrieve them from HMDB or CTD.
**Key principles**:
1. **LIPID MAPS classification first** — use the 8-category system (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, polyketides)
2. **Structural specificity matters** — chain length, unsaturation, and sn-position affect biological function
3. **Connect to pathways** — lipids are metabolic intermediates; always map to biosynthesis/degradation pathwaysView the complete source on GitHubtooluniverse-literature-deep-research
When your agent uses it
Deep literature review — PubMed, EuropePMC, bioRxiv preprints, citation networks, evidence synthesis. Disambiguates queries, runs collision-aware searches, grades evidence T1-T4, and produces structured reports. Use for systematic literature review, meta-analysis evidence collection, and detailed answer-with-citations workflows.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
4,160 tokensWhen the skill is activated
Reference documents
21,105 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Workflow
- Phase 0: Mode Selection
- Phase 1: Subject Disambiguation + Profile
- Phase 2: Literature Search
- Phase 3: Evidence Grading
- Report Output
- Communication
- References
Files
EXAMPLES.mddoc26,860 bytesFULLTEXT_STRATEGY.mddoc3,807 bytesQUICK_REFERENCE.mddoc6,208 bytesREPORT_TEMPLATE.mddoc12,892 bytesSKILL.mdcore17,062 bytesSKILL.mdcore17,062 bytesSKILL.mdcore17,062 bytesSKILL.mdcore17,062 bytesSKILL.mdcore17,062 bytesTOOL_NAMES_REFERENCE.mddoc22,206 bytesWORKFLOW.mddoc7,827 bytes
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# Literature Deep Research
Systematic literature research: disambiguate, search with collision-aware queries, grade evidence, produce structured reports.
**KEY PRINCIPLES**: (1) Disambiguate first (2) Right-size deliverable (3) Grade every claim T1-T4 (4) All sections mandatory even if "limited evidence" (5) Source attribution for every claim (6) English-first queries, respond in user's language (7) Report = deliverable, not search log
---
## LOOK UP, DON'T GUESS
Search PubMed/EuropePMC FIRST before reasoning. A published paper beats memory.
**Factoid search strategy:**
1. Extract KEY TERMS (most specific nouns/verbs)View the complete source on GitHubtooluniverse-medical-imaging-radiology
When your agent uses it
Discover and characterize cancer radiology imaging cohorts from The Cancer Imaging Archive (TCIA) — list public collections (e.g. LIDC-IDRI, TCGA-GBM, TCGA-LUAD, TCGA-BRCA), enumerate patients/subjects and studies within a collection, browse DICOM series by modality (CT, MR, PT, CR, DX, US, NM) or body part (CHEST, BRAIN, ABDOMEN, ...), pull series-level DICOM metadata and scanner manufacturer, estimate download size before pulling data, and enumerate individual DICOM instance (slice) UIDs within a series. Use when someone asks to "find a CT/MRI/PET imaging cohort for [cancer type]", "how many patients/studies/series are in [TCIA collection]", "what imaging modalities/body parts/scanners does [collection] have", "look up DICOM series metadata for [SeriesInstanceUID]", "estimate the size of this TCIA series before downloading", or "build a radiology imaging research cohort". NOT for microscopy/cell imaging quantification (use tooluniverse-image-analysis), NOT for actual DICOM pixel data analysis, segmentation, or diagnosis (TCIA tools here return metadata only — no pixel access), NOT for tumor genomic/omics data (use tooluniverse-cancer-genomics-tcga).
What it loads into context
Frontmatter
296 tokensAlways available to the agent
SKILL.md
1,848 tokensWhen the skill is activated
Reference documents
2,468 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Scope: what this skill can and cannot do
- When to Use This Skill
- Tool Reference
- Workflow
- Notes
- References
Files
SKILL.mdcore7,560 bytesreferences/tcia_tool_reference.mddoc7,524 bytes
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# Cancer Radiology Imaging Cohort Discovery (TCIA)
Discover and characterize public cancer imaging cohorts from The Cancer
Imaging Archive (TCIA, National Cancer Institute) — collection, patient,
study, and DICOM series-level metadata for building or scoping a radiology
research cohort.
## Scope: what this skill can and cannot do
**Can:** enumerate collections, patients, studies, and series; filter by
modality/body part/manufacturer; pull DICOM series-level metadata (scanner,
acquisition, license); estimate series download size; list individual DICOM
instance (slice) UIDs.
View the complete source on GitHubtooluniverse-mendelian-randomization
When your agent uses it
Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS association lookups (use the GWAS skills) or fitting your own instruments from raw summary statistics.
What it loads into context
Frontmatter
254 tokensAlways available to the agent
SKILL.md
2,672 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The three instrumental-variable assumptions
- When to use
- Anchor tools
- Workflow
- Interpretation tables
- Limitations (state these honestly)
- Reporting template
Files
SKILL.mdcore10,999 bytes
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# Mendelian Randomization (Causal Inference from Genetic Instruments)
**MR estimates the CAUSAL effect of an exposure on an outcome using genetic variants as instrumental variables.** Because alleles are randomized at conception, MR is largely robust to the confounding and reverse causation that bias observational associations. It is *not* a free lunch: the causal claim rests on three assumptions, and violating them (especially horizontal pleiotropy) silently biases the estimate.
**LOOK UP, DON'T GUESS:** never assert a causal MR estimate from memory. Genetic-instrument results are updated as new GWAS are published — always retrieve current evidence with `EpiGraphDB_get_mendelian_randomization`. Do not invent beta/p-values.
**Correlation ≠ causation, and genetic correlation ≠ causation.** A high genetic correlation (`rg`) means two traits share heritability — it does NOT establish a causal direction. Only MR (with valid instruments) speaks to causality. Report them as different kinds of evidence.
## The three instrumental-variable assumptions
| Assumption | Statement | How it fails | Check |
|---|---|---|---|
| **Relevance** | Instrument is robustly associated with the exposure | Weak instruments (low F-stat) → bias toward the confounded observational estimate | MOE score; instruments selected at GWAS significance |
| **Independence** | Instrument shares no common cause with the outcome | Population stratification, assortative mating | Ancestry-matched GWAS; report population |View the complete source on GitHubtooluniverse-meta-analysis
When your agent uses it
Meta-analysis / evidence synthesis — pool effect sizes across studies (odds ratios, risk ratios, hazard ratios, mean differences, correlations, GWAS betas) with fixed- or random-effects models, quantify heterogeneity (Q, I², τ²), and build a forest plot. Use when you have results from MULTIPLE studies and need a single pooled estimate, or to synthesize evidence from a systematic review / multiple GWAS / replicated experiments. Handles the error-prone effect-size + standard-error preparation (converting OR/HR/CI, two-group means±SD, proportions, and correlations into the (effect, SE) the pooling step needs).
What it loads into context
Frontmatter
150 tokensAlways available to the agent
SKILL.md
2,126 tokensWhen the skill is activated
Reference documents
4,088 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- The workflow
- Step 1 — Convert each study to (effect_size, se) ← do this carefully
- Step 2 — Fixed vs random effects
- Step 3 — Pool with MetaAnalysis_run
- Step 4 — Interpret heterogeneity (decides the story)
- Step 5 — Forest plot + report
- Honest limitations
- Related skills
Scripts executed separately
scripts/meta_analysis.py
Files
SKILL.mdcore8,065 bytesSKILL.mdcore8,065 bytesSKILL.mdcore8,065 bytesscripts/meta_analysis.pyruns6,149 bytes../tooluniverse-statistical-modeling/references/cox_regression.mddoc14,247 bytes
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# Meta-Analysis & Evidence Synthesis
Pool quantitative results from **multiple studies** into one estimate, and judge how consistent the studies are. This is the statistical half of a systematic review (the literature-collection half is `tooluniverse-literature-deep-research`).
## When to use this
- You have effect sizes from ≥2 studies/cohorts and want a single pooled estimate + CI.
- Synthesizing a systematic review, replicated experiments, multi-cohort GWAS, or multi-dataset associations.
- Deciding whether studies agree (low heterogeneity) or conflict (high heterogeneity).
Do NOT use it to *find* the studies — use `tooluniverse-literature-deep-research` / the literature tools for that, then bring the extracted numbers here. Before trusting any input study, consider checking it with `Crossref_check_retraction`.
## The workflow
View the complete source on GitHubtooluniverse-metabolomics
When your agent uses it
Metabolomics research — metabolite identification, study analysis, and database searches across HMDB, MetaboLights, Metabolomics Workbench, KEGG. Use for annotating mass-spec features to known metabolites, finding metabolomics studies of a disease, and structured metabolomics research reports with metabolite-pathway mapping.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
3,050 tokensWhen the skill is activated
Reference documents
5,157 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Use Case
- Databases Covered
- Research Workflow
- Usage Patterns
- Input Parameters
- Output Format
- Implementation Notes
- Tool Discovery
- Common Issues
- Summary
- Reasoning Framework
Scripts executed separately
diabetes_analysis.pypython_implementation.pyscripts/metabolism_ref.py
Files
.env.templateasset499 bytesQUICK_START.mddoc11,961 bytesSKILL.mdcore13,260 bytesdiabetes_analysis.pyruns1,724 bytesexample1_metabolites.mddoc722 bytesexample2_study.mddoc631 bytesexample3_search.mddoc861 bytespython_implementation.pyruns10,615 bytesscripts/metabolism_ref.pyruns9,745 bytestest1_metabolites.mddoc1,011 bytestest2_study.mddoc753 bytestest3_search.mddoc859 bytestest4_comprehensive.mddoc1,663 bytes
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# Metabolomics Research
Comprehensive metabolomics research skill that identifies metabolites, analyzes studies, and searches metabolomics databases. Generates structured research reports with annotated metabolite information, study details, and database statistics.
## Use Case
**Use this skill when asked to:**
- Identify or annotate metabolites (HMDB IDs, chemical properties, pathways)
- Retrieve metabolomics study information from MetaboLights or Metabolomics Workbench
- Search for metabolomics studies by keywords or disease
- Analyze metabolite profiles or datasets
- Generate comprehensive metabolomics research reports
**Example queries:**View the complete source on GitHubtooluniverse-metabolomics-analysis
When your agent uses it
Analyze metabolomics data end-to-end — metabolite identification, quantification (TIC normalization, batch correction), differential analysis, and pathway interpretation. Use for processing mass-spec metabolomics output, normalization choice, untargeted metabolomics workflows, and integrating with other omics layers.
What it loads into context
Frontmatter
75 tokensAlways available to the agent
SKILL.md
3,433 tokensWhen the skill is activated
Reference documents
3,267 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- When to Use This Skill
- Core Capabilities
- Workflow Overview
- Phase Summaries
- Integration with ToolUniverse
- Quantified Minimums
- Limitations
- References
- Reference Files
Files
SKILL.mdcore14,187 bytesSKILL.mdcore14,187 bytescode_examples.mddoc8,203 bytesreport_template.mddoc4,001 bytes
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# Metabolomics Analysis
Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.
## Domain Reasoning
Metabolomics quantification depends critically on normalization. Total ion current (TIC) normalization corrects for sample-loading variation and works well for global abundance changes; internal standard normalization is more accurate for targeted analysis where specific metabolite concentrations matter. Missing values in a peak table may reflect signal below the detection limit — not true absence — and should be imputed or handled explicitly rather than treated as zero. Failing to account for batch effects across instrument runs is a frequent source of spurious differential metabolites.
## LOOK UP DON'T GUESS
- Metabolite identities: use `Metabolite_search` and `Metabolite_get_info` to confirm names, CIDs, and HMDB IDs; never assume identity from m/z alone.
- Pathway memberships: query KEGG, MetaCyc, or Reactome tools; do not list pathways from memory.
- Disease associations: retrieve from CTD via `Metabolite_get_diseases`; do not infer clinical relevance without database evidence.
- CV thresholds and QC criteria: apply the values defined in this workflow (CV < 30%, blank ratio > 3x); do not override with guesses.View the complete source on GitHubtooluniverse-metabolomics-pathway
When your agent uses it
Metabolomics pathway analysis — metabolite identification (HMDB, KEGG, ChEBI), pathway mapping (Reactome, KEGG, MetaCyc), disease associations, enzyme/gene linkage. Use for metabolite-to-pathway-to-disease connections, BridgeDb-based ID conversion, and integrating metabolomics with gene-level pathway analyses.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
1,954 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Workflow
- Phase 0: Metabolite Identification & Resolution
- Phase 1: Metabolite Characterization
- Phase 2: Pathway Mapping
- Phase 3: Enzyme & Gene Linkage
- Phase 4: Disease Associations
- Phase 5: Cross-Database Enrichment
- Common Mistakes to Avoid
- Fallback Strategies
- Evidence Grading
- Limitations
Files
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# Metabolomics Pathway Analysis
Identify metabolites, map to metabolic pathways, find disease associations, and connect to enzymes/genes.
## Domain Reasoning
Metabolite-to-pathway mapping requires correct, database-specific identifiers. HMDB IDs link to KEGG/Reactome but must be converted via BridgeDb; PubChem CIDs need explicit cross-referencing. Always verify metabolite identity first: the same common name can refer to structurally distinct isomers, and PubChem names frequently differ from CTD/KEGG names.
## LOOK UP DON'T GUESS
- Pathway membership: call `MetaCyc_get_compound`, `KEGG_get_compound`, or `ReactomeContent_search`
- Cross-database IDs: use `BridgeDb_xrefs`
- Enzyme-metabolite relationships: use `CTD_get_chemical_gene_interactions` or `KEGG_get_compound`
- Disease associations: query `Metabolite_get_diseases` or `CTD_get_chemical_diseases`View the complete source on GitHubtooluniverse-metagenomics-analysis
When your agent uses it
Microbiome and metagenomics analysis using MGnify, GTDB taxonomy, ENA sequencing data, and EuropePMC literature. Covers taxonomic classification, genome quality assessment, biome-clinical phenotype linkage, and pathway interpretation. Use for amplicon/shotgun metagenomics study analysis.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
1,274 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Core Databases
- Workflow
- Key Phase Notes
- Edge Cases & Fallbacks
- Limitations
Files
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# Metagenomics & Microbiome Analysis
Integrated pipeline for exploring microbiome studies, classifying taxa, assessing genome quality, linking microbial composition to clinical phenotypes, and interpreting findings through pathway analysis and literature context.
**Guiding principles**:
1. **Study context first** -- understand biome, sequencing method, and metadata before diving into taxa
2. **Taxonomic consistency** -- GTDB taxonomy as reference standard; reconcile NCBI where needed
3. **Genome quality matters** -- CheckM completeness/contamination thresholds determine trustworthy MAGs
4. **Interpretation over enumeration** -- explain what taxa mean for the biological question
5. **English-first queries** -- use English terms in tool calls
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.
View the complete source on GitHubtooluniverse-microbial-genome-characterization
When your agent uses it
Genome-ASSEMBLY discovery, QC, and replicon mapping for any organism (bacteria, archaea, fungi, and beyond) using NCBI Datasets. Resolves an organism name or taxid to assemblies, picks the reference/representative or best-quality assembly, pulls assembly QC metrics (total length, contig/scaffold N50, contig count, GC%, assembly level, RefSeq category), enumerates chromosomes and plasmids via per-replicon sequence reports, and compares candidate assemblies on quality. Use for "what genomes are available for [organism]", "assembly stats / N50 / GC content for [GCF_/GCA_ accession]", "how many plasmids does [strain] have", "compare assemblies for [species]", "find the reference genome for [taxon]", "is this assembly Complete Genome or just contigs". NOT for gene-level orthology/synteny (use tooluniverse-comparative-genomics), plant gene structure (use tooluniverse-plant-genomics), de novo assembly from raw reads (no tool exists), or taxonomy-only name/lineage lookups.
What it loads into context
Frontmatter
250 tokensAlways available to the agent
SKILL.md
3,406 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Tools (all keyless, verified live)
- Workflow
- Interpretation Table
- Worked Example 1 — Reference genome of *E. coli* (single chromosome, no plasmid)
- Worked Example 2 — "How many plasmids does *E. coli* O157:H7 Sakai have?"
- Worked Example 3 — Inventory scale (large taxon)
- Limitations
- Completeness Checklist
Files
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# Microbial Genome Assembly Characterization & QC
Discover, quality-control, and structurally map genome ASSEMBLIES for any organism using the keyless NCBI Datasets genome tools. Organism/taxon in → assembly inventory, QC metrics, and chromosome/plasmid map out.
## LOOK UP, DON'T GUESS
When uncertain about an accession, assembly level, replicon count, or N50, CALL the tool. Never report assembly statistics from memory — accessions and metrics change with each RefSeq release. A live NCBI Datasets answer is always more reliable than a guess.
## COMPUTE, DON'T DESCRIBE
When comparing multiple assemblies or ranking by quality, retrieve each via the tools, then write and run Python (pandas) over the returned JSON to sort, score, and tabulate. Don't describe what you would compute — execute it and report actual numbers.
## When to Use This Skill
**Triggers**:
- "What genomes are available for [organism]?" / "Find the reference genome for [taxon]"View the complete source on GitHubtooluniverse-microbiome-research
When your agent uses it
Microbiome research using MGnify, GTDB, ENA, OLS (ENVO biomes), EuropePMC, BacDive (strain phenotypes), MediaDive (growth media recipes), and GMrepo (gut microbiome disease-association counts). Covers study discovery, taxonomic profiling, host-microbe interaction analysis, biome-by-condition queries, strain culturing requirements, and phenotype/disease prevalence lookups. Use for microbiome study selection, organism-environment associations, clinical-microbiome literature review, and "how do I grow this organism" / "how prevalent is this species in condition X" questions. Distinct from analytical workflow (use tooluniverse-metagenomics-analysis for that).
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
4,235 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Tools
- Quick Start
- Common Workflows
- MGnify Biome Hierarchy
- Key Identifiers
- Reasoning Framework
- Tips
Files
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# Microbiome Research with ToolUniverse
Comprehensive microbiome analysis using MGnify (EBI metagenomics), GTDB (genome taxonomy), ENA (sequencing data), OLS (ontology lookup for ENVO biomes), and EuropePMC (literature).
## Core Tools
| Tool | Purpose | Auth |
|------|---------|------|
| **MGnify_search_studies** | Find metagenomics studies by biome/keyword | None |
| **MGnify_get_study_detail** | Study metadata, abstract, sample counts | None |
| **MGnify_list_analyses** | List taxonomic/functional analysis outputs for a study | None |
| **MGnify_get_taxonomy** | Taxonomic composition from an analysis | None |
| **MGnify_get_go_terms** | GO functional annotations from an analysis | None |
| **MGnify_get_interpro** | InterPro protein domain annotations | None |View the complete source on GitHubtooluniverse-ml-inference-utility
When your agent uses it
Generic ML/NLP/vision inference against any HuggingFace-hosted model (serverless hf-inference provider) — text classification/sentiment, dense text embeddings for semantic similarity, masked-token prediction (including protein language models), summarization, zero-shot classification against caller-supplied labels, named entity recognition, extractive question answering, translation, image classification, and object detection. Use when someone asks to "classify this text with a HuggingFace model", "embed this text for similarity search", "run NER on this passage", "summarize this with model X", "zero-shot classify this against these labels", "predict the masked token", "classify/detect objects in this image", or names a specific HuggingFace model_id to run inference with. NOT a domain-analysis skill itself — this is a generic cross-domain utility other skills should reach for as a supporting step, the same way tooluniverse-clinical-terminology-lookup is a supporting lookup step rather than a research domain. NOT for training/fine-tuning models (inference only, against already-hosted models). Honest: as of this writing, HuggingFace's serverless inference API requires a valid HF_TOKEN for every one of these 10 tools, on every model tested — live-verified, not assumed from the "optional" wording in the tools' own descriptions. Also covers finding a model_id first via HuggingFace Hub search (HuggingFace_search_models/get_model/search_datasets — a separate, token-free catalog API) and running inference on Replicate-hosted models (Replicate_run_prediction/get_prediction) as an alternative platform when a model isn't served by HuggingFace's hf-inference provider.
What it loads into context
Frontmatter
370 tokensAlways available to the agent
SKILL.md
3,860 tokensWhen the skill is activated
Reference documents
1,880 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first) — the token requirement is not actually optional
- When to Use This Skill
- The 10 Tools
- Choosing a `model_id`
- Workflow
- Finding a model first: HuggingFace Hub search (a different API, no token needed)
- An alternative inference platform: Replicate
- Reference
Files
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# Generic ML/NLP/Vision Inference (HuggingFace)
Run any HuggingFace-hosted model for a standard inference task via ToolUniverse's
10 `HFInference_*` tools. This is a **utility skill**: reach for it as a supporting
step inside another workflow (e.g. summarizing a batch of retrieved abstracts,
extracting entities from clinical text, embedding sentences for a similarity
search) rather than treating it as a research domain in its own right.
## Honesty contract (read first) — the token requirement is not actually optional
Every tool's own JSON description says `HF_TOKEN` is an *optional* API key that
merely "raises rate limits." **Live testing on this date contradicts that**: all
10 tools, tested against the exact model IDs in their own `test_examples`, fail
with the identical error with no token set:View the complete source on GitHubtooluniverse-model-organism-genetics
When your agent uses it
Cross-species genetic analysis using model organism databases (MGI mouse, IMPC systematic knockout phenotyping, ZFIN zebrafish, FlyBase fruit fly, WormBase worm, SGD budding yeast plus protein-domain/PTM/literature detail, PomBase fission yeast, RGD rat, GeneNetwork systems-genetics/eQTL panels, HumanMine/MouseMine cross-species data-warehouse search, GBIF taxonomy) plus VEuPathDB for eukaryotic pathogens (Plasmodium, Toxoplasma, fungi, vectors). Maps human genes to orthologs, retrieves phenotype/expression/functional data, assesses gene function conservation, and identifies the best animal model for studying a human gene or disease.
What it loads into context
Frontmatter
167 tokensAlways available to the agent
SKILL.md
8,987 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- Reasoning Principles
- Pipeline
- Bacterial and Classical Genetics Reasoning
- Completeness Checklist
Files
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Model Organism Genetics Pipeline
Map human genes to model organism orthologs and retrieve phenotype, expression, and functional data across six species. Synthesize cross-species evidence to assess gene function conservation and identify the best animal models for studying human genes and diseases.
**Not for**: human variant interpretation (`tooluniverse-variant-analysis`), drug target validation (`tooluniverse-drug-target-validation`), human disease characterization (`tooluniverse-multiomic-disease-characterization`), pathogen outbreak/drug-repurposing intelligence once a VEuPathDB target gene is identified (`tooluniverse-infectious-disease`).
**LOOK UP, DON'T GUESS**: When asked about a species' taxonomy, ecology, or biology, search GBIF/NCBI Taxonomy first. For GBIF: use `GBIF_search_species(query="species name")`, then use the `nubKey` (not `key`) from the result to call `GBIF_get_species(speciesKey=nubKey)` for full taxonomy (kingdom, phylum, class, order, family). The `nubKey` is the GBIF backbone key; the `key` is dataset-specific and often lacks higher taxonomy.
---
## Reasoning PrinciplesView the complete source on GitHubtooluniverse-molecular-cloning
When your agent uses it
Molecular cloning, in both directions. DESIGN — Gibson Assembly (overlap design for seamless multi-fragment joining) and Golden Gate Assembly (Type IIS / BsaI / BbsI / Esp3I / BsmBI / SapI design with unique 4-bp fusion overhangs). ANALYSIS — work out what an existing reaction produces: given input plasmid sequences and an enzyme, digest them, join the fragments by their overhangs, and identify features of the product (expressed ORF, gRNA spacer and its target gene). Use when you need to plan how to join DNA fragments into a construct, design assembly overlaps/overhangs, decide between cloning methods, or determine the product of a stated Gibson/Golden Gate reaction. Covers the domestication (internal-site removal), overhang-uniqueness, and overlap-Tm rules. For PCR primers to generate the fragments, see tooluniverse-primer-design.
What it loads into context
Frontmatter
210 tokensAlways available to the agent
SKILL.md
1,942 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Step 0 — Pick the method
- Step 1 — Gibson Assembly
- Step 2 — Golden Gate Assembly
- Step 3 — QC before ordering
- Step 4 — Gotchas (state these)
- Working backwards — what does an existing assembly produce?
- Honest limitations
- Related skills
Scripts executed separately
scripts/cloning_qc.py
Files
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# Molecular Cloning Assembly Design (Gibson & Golden Gate)
Plan how to join DNA fragments into a construct: design the **overlaps** (Gibson) or **Type IIS overhangs** (Golden Gate) and avoid the failures that come from internal sites and non-unique junctions.
## Step 0 — Pick the method
| Use **Gibson Assembly** when | Use **Golden Gate** when |
|---|---|
| A few fragments, **scarless/seamless** junctions anywhere you choose | Many parts, **standardized reusable** parts (MoClo/modular), one-pot |
| You can add ~20–40 bp homology by PCR | You can remove internal BsaI/BbsI sites (domestication) |
| One-off constructs | Combinatorial libraries / repeated assemblies |
Both are sequence-independent (no scar at the junction for Gibson; a 4-bp fusion scar for Golden Gate). For 2–4 unique fragments, Gibson is usually simplest; for libraries or a parts toolkit, Golden Gate.
View the complete source on GitHubtooluniverse-multi-omics-integration
When your agent uses it
Multi-omics integration — orchestrate per-layer analysis (transcriptomics, proteomics, epigenomics, genomics, metabolomics) then perform cross-omics correlation, multi-omics clustering, and pathway-level integration. Use for integrative systems-biology analysis, multi-modal disease characterization, and cross-omics biomarker discovery.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,800 tokensWhen the skill is activated
Reference documents
2,357 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- When to Use This Skill
- Workflow Overview
- Supported Data Types
- Core Operations
- Multi-Omics Clustering Methods
- ToolUniverse Skills Coordination
- Use Cases
- Quantified Minimums
- Limitations
- References
- Detailed Reference
Files
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# Multi-Omics Integration
Coordinate and integrate multiple omics datasets for comprehensive systems biology analysis. Orchestrates specialized ToolUniverse skills to perform cross-omics correlation, multi-omics clustering, pathway-level integration, and unified interpretation.
---
## Domain Reasoning
Multi-omics integration asks whether different molecular layers tell a concordant story. If a gene is upregulated in RNA-seq AND its protein is elevated in proteomics, that is concordant evidence of true biological change. Discordance — high mRNA but low protein, or elevated protein without matching mRNA — may indicate post-transcriptional regulation (miRNA silencing, protein degradation, translational control) and is itself a meaningful finding worth reporting. Not every discordance is noise; some are the most interesting biology.
## LOOK UP DON'T GUESS
- Expected RNA-protein correlation ranges: compute Spearman r from the actual data; the typical range (0.4-0.6) is a guide, not a guarantee.
- Pathway enrichment results: run `ReactomeAnalysis_pathway_enrichment` or gseapy on the actual gene lists; never list enriched pathways from memory.View the complete source on GitHubtooluniverse-multiomic-disease-characterization
When your agent uses it
Comprehensive disease characterization across genomics, transcriptomics, proteomics, and pathways for systems-level understanding. Identifies therapeutic opportunities and biomarker candidates by integrating multi-layer molecular data. Use for full-omics disease deep-dive reports, mechanism mapping, and biomarker-and-target identification from multi-omics data.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
2,011 tokensWhen the skill is activated
Reference documents
9,265 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Input Parameters
- Pipeline Overview
- Key Tool Parameter Notes
- Reference Files
Files
.env.templateasset499 bytesSKILL.mdcore8,911 bytesintegration-scoring.mddoc4,491 bytesreport-template.mddoc7,546 bytesresponse-formats.mddoc2,415 bytestool-reference.mddoc16,089 bytesuse-patterns.mddoc3,592 bytes
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# Multi-Omics Disease Characterization Pipeline
Characterize diseases across multiple molecular layers (genomics, transcriptomics, proteomics, pathways) to provide systems-level understanding of disease mechanisms, identify therapeutic opportunities, and discover biomarker candidates.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Disease disambiguation FIRST** - Resolve all identifiers before omics analysis
3. **Layer-by-layer analysis** - Systematically cover all omics layers
4. **Cross-layer integration** - Identify genes/targets appearing in multiple layers
5. **Evidence grading** - Grade all evidence as T1 (human/clinical) to T4 (computational)
6. **Tissue context** - Emphasize disease-relevant tissues/organs
7. **Quantitative scoring** - Multi-Omics Confidence Score (0-100)
8. **Druggable focus** - Prioritize targets with therapeutic potential
9. **Biomarker identification** - Highlight diagnostic/prognostic markersView the complete source on GitHubtooluniverse-natural-product-dereplication
When your agent uses it
Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact mass/InChIKey/SMILES)", or "classify this molecule into ChemOnt". Searches NPAtlas for known microbial natural products (producing organism + literature reference), assigns the ChemOnt kingdom→superclass→class→subclass hierarchy via ClassyFire, resolves systematic IUPAC names to structure via OPSIN, and cross-references identity in PubChem. NOT for general drug/compound identity or ADMET (use tooluniverse-chemical-compound-retrieval / tooluniverse-small-molecule-discovery) and NOT for metabolomics pathway/enrichment analysis (use tooluniverse-metabolomics skills).
What it loads into context
Frontmatter
205 tokensAlways available to the agent
SKILL.md
3,956 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Backing Tools (all keyless; verify before quoting)
- Workflow
- Interpretation Guidance (dereplication logic)
- Worked Example — Staurosporine (trivial name in)
- Worked Example — Acetylsalicylic acid (systematic name in)
- Limitations (be honest in every report)
- Out of Scope → route elsewhere
- Completeness Checklist
Files
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# Natural Product Dereplication & Chemotaxonomy
Decide whether a putative natural product is **already known**, identify the **microbe that produces it**, attach the **literature reference**, and assign its **ChemOnt chemical class**. This is the dereplication question every NP chemist and metabolomics analyst asks of a new feature: *"have we seen this before, and what makes it?"*
**LOOK UP DON'T GUESS**: Never assume an NPAID, producing organism, exact mass, or chemical class. Every identity, provenance, and taxonomy claim must come from a live tool call.
**Scope (microbial NPs only)**: NPAtlas covers natural products from **bacteria and fungi**. It does NOT cover plant, animal, or marine-invertebrate metabolites unless a microbial producer was reported. A "no NPAtlas hit" therefore means *not a known microbial NP* — it does not prove the molecule is novel in an absolute sense.
---
## Backing Tools (all keyless; verify before quoting)
| Tool | Input | Returns |
|------|-------|---------|View the complete source on GitHubtooluniverse-network-pharmacology
When your agent uses it
Compound-target-disease network construction and analysis for drug repurposing, polypharmacology discovery, and multi-target drug design. Uses STRING, BioGRID, ChEMBL, DGIdb, OMIM, OpenTargets. Use for off-target effect prediction, network-based drug repurposing, and identifying molecules with desired multi-target profile.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
3,322 tokensWhen the skill is activated
Reference documents
37,398 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- Polypharmacology Reasoning (Start Here)
- When to Use This Skill
- Key Principles
- Network Pharmacology Score (0-100)
- Workflow Overview
- Critical Tool Parameter Notes
- Fallback Strategies
- Reference Files
- Related Skills
Scripts executed separately
scripts/network_proximity.py
Files
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Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Network Pharmacology Pipeline
Construct and analyze compound-target-disease (C-T-D) networks to identify drug repurposing opportunities, understand polypharmacology, and predict drug mechanisms using systems pharmacology approaches.
**LOOK UP DON'T GUESS** - Retrieve actual target lists, network data, and clinical evidence from tools. Do not infer network relationships from drug class alone.
**IMPORTANT**: Always use English terms in tool calls, even if the user writes in another language. Respond in the user's language.
---
## Polypharmacology Reasoning (Start Here)View the complete source on GitHubtooluniverse-neuroscience
When your agent uses it
Neuroscience research workflows: neuroanatomy, neural circuits, neurotransmitter biology, neurological/psychiatric disease genetics, neural-protein function. Uses Allen Brain Atlas, WormBase (C. elegans connectome), UniProt for neural proteins, PubMed for primary literature. Use for brain-region biology, neural development, neurodegeneration mechanisms (Alzheimer's, Parkinson's, ALS), and synaptic-protein characterization.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
6,819 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- 1. Computational Neuroscience Reasoning
- 2. Neuroanatomy Reasoning
- 3. Clinical Neurology Reasoning
- 4. Neurophysiology Reasoning
- 5. Available Tools
- 6. C. elegans Connectome Lookups
- 7. Single-Neuron Morphology Lookups (NeuroMorpho)
- 8. Neuroimaging Dataset Discovery (OpenNeuro)
- 9. Computational Model Lookups (ModelDB)
- 10. Neuroimaging Statistical Map Discovery (NeuroVault)
- 11. Choosing Among the Four Neuroscience Data Resources
- 12. Common Pitfalls
Files
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Read SKILL.md
# Neuroscience Research Skill
**KEY PRINCIPLES**: LOOK UP, DON'T GUESS — use PubMed/EuropePMC for neuroanatomy facts, WormBase for C. elegans connectome data, UniProt for neural protein properties. Verify claims with literature before answering. Use Python computation for quantitative neuroscience problems.
---
## LOOK UP, DON'T GUESS
When uncertain about any neuroscience fact — brain region function, neural circuit connectivity, ion channel properties, neurotransmitter receptor subtypes — SEARCH databases first. A PubMed-verified answer is always more reliable than reasoning from memory. This is especially critical for neuroanatomy, where structures have precise boundaries and connectivity patterns that are easy to confuse.
---
## 1. Computational Neuroscience Reasoning
### Rate-Based ModelsView the complete source on GitHubtooluniverse-neurosynth-meta-analysis
When your agent uses it
Meta-analytic decoding of human fMRI coordinates using the Neurosynth coordinate database (14,371 studies, ~3,228 cognitive/neuroscience terms) — given an MNI brain coordinate, statistically identify which cognitive terms are over-represented in studies reporting activation there ("reverse inference"); given a term, find the studies and coordinates most associated with it ("forward" lookup). Use when someone asks "what does this brain coordinate/region do", "decode this fMRI activation peak", "what terms are associated with [MNI coordinate]", "find studies about [cognitive term] and their activation coordinates", or "reverse-infer cognitive function from a brain location". This is real local analysis on a downloaded, cached coordinate database — not a live API — following ToolUniverse's honesty-contract pattern (real computed statistics, no fabricated term associations).
What it loads into context
Frontmatter
190 tokensAlways available to the agent
SKILL.md
1,785 tokensWhen the skill is activated
Reference documents
1,343 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to Use This Skill
- Setup
- Workflow
- Reference
Scripts executed separately
scripts/_neurosynth_data.pyscripts/decode_coordinate.pyscripts/download_data.pyscripts/search_by_term.py
Files
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Read SKILL.md
# Neurosynth Meta-Analytic Coordinate Decoding
Statistically decode human fMRI brain coordinates against ~14,000 published neuroimaging studies, or look up which studies/coordinates are associated with a cognitive term — using the real, freely-downloadable Neurosynth coordinate database (no API key, no login).
## Honesty contract (read first)
This skill downloads a real coordinate database and runs real statistics on it. It must never fabricate a term association, a study result, or a p-value.
1. **Preflight before anything.** Run `scripts/download_data.py` first. If any file fails to download, STOP and report the failure — do not proceed with partial data or describe hypothetical decoding results.
2. **Never guess a term.** `search_by_term.py` requires an exact (case-sensitive) vocabulary match. If the term isn't found, it returns suggestions (substring matches, or close spellings when there are none, e.g. `hipocampus` → `hippocampus`) — use those, don't invent a plausible-sounding term.
3. **Report the multiple-testing caveat every time.** `decode_coordinate.py` tests ~3,228 terms simultaneously per query. Uncorrected p-values will produce false positives at this scale — always surface the FDR-corrected `fdr_q_value`, not just the raw `p_value`, when characterizing how confident a result is.
4. **This is a simplified reverse-inference test, not a full meta-analysis pipeline.** It does binary term-presence comparison (near-coordinate studies vs. rest of corpus) via a two-proportion z-test — a real, defensible statistical test, but not the same as running NiMARE's full activation-likelihood-estimation (ALE) or multilevel kernel density analysis (MKDA) methods. Say so if precision matters (e.g., the user is drafting something for publication).
5. **Never overwrite the cached data files** — `download_data.py` writes to a dedicated cache directory and is idempotent (skips re-download if already cached, unless `--force`).
View the complete source on GitHubtooluniverse-nfcore-pipelines
When your agent uses it
Run real end-to-end bioinformatics pipelines (nf-core rnaseq, sarek, atacseq) via Nextflow + Docker/Singularity, for raw FASTQ/BAM data — either local files or public GEO/SRA datasets. Use when someone asks to "run RNA-seq alignment", "quantify gene expression from FASTQs", "call germline or somatic variants from my WGS/WES data", "run an nf-core pipeline", "analyze ATAC-seq chromatin accessibility data", "reanalyze a GEO/SRA dataset (GSE/GSM/SRR accession)", "generate a samplesheet for nf-core", or "set up Nextflow to process my sequencing data". Orchestrates the full pipeline (alignment/quantification, or variant calling, or peak calling) rather than a single step. Honest: shells out to real Nextflow/Docker; checks the environment first and stops with install instructions rather than fabricating pipeline output; always runs the pipeline's small test profile before real data.
What it loads into context
Frontmatter
226 tokensAlways available to the agent
SKILL.md
3,137 tokensWhen the skill is activated
Reference documents
3,228 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to use vs. not
- Requirements (be upfront about these)
- Workflow Checklist
- Step 0: Acquire Data (GEO/SRA only)
- Step 1: Environment Check
- Step 2: Select Pipeline
- Step 3: Run Test Profile
- Step 4: Generate Samplesheet
- Step 5: Configure & Run
- Step 6: Verify Outputs
- References
- Disclaimer
Scripts executed separately
scripts/check_environment.pyscripts/detect_data_type.pyscripts/generate_samplesheet.py
Files
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Read SKILL.md
# nf-core Pipeline Orchestration
Run real nf-core bioinformatics pipelines end-to-end for bench scientists who
need alignment/quantification, variant calling, or chromatin-accessibility
analysis without hand-building a pipeline.
## Honesty contract (read first)
This skill drives **real Nextflow pipelines** on **real compute**. It must
never fabricate a pipeline result.
1. **Preflight before anything.** Run `scripts/check_environment.py`. If
Docker, Nextflow (>=23.04), or Java (>=11) is missing, print the install
plan and STOP. Do not describe hypothetical alignment/variant-callingView the complete source on GitHubtooluniverse-nih-funding-landscape
When your agent uses it
Analyze NIH grant portfolios and funding history using the OpenNIH FY1985-present corpus, then connect grants to investigators, institutions, publications, clinical trials, patents, targets, or drugs with ToolUniverse. Use for NIH grant discovery, topic or Institute/Center trends, PI and institution profiles, activity-code or mechanism analysis, funding growth and concentration, grant-writing landscape research, SBIR/STTR landscapes, research-policy analysis, expert discovery, funding-to-output translational impact studies, or public-facing NIH explainers for patients, advocates, local and national journalists, trainees, applicants, institutions, entrepreneurs, and taxpayers.
What it loads into context
Frontmatter
137 tokensAlways available to the agent
SKILL.md
5,662 tokensWhen the skill is activated
Reference documents
18,351 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Public Value Routing
- Required Workflow
- Endpoint-Specific Interpretation
- Analysis Rules
- Quantified Completeness
- Cross-Source Linking
- Output
- Quality Check
Scripts executed separately
scripts/verify_live_cases.pyscripts/verify_live_contract.pyscripts/verify_output_links.py
Files
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Read SKILL.md
# NIH Funding Landscape
Build source-traceable NIH funding analyses with explicit scopes, count units, data coverage, and interpretation limits. Use `OpenNIH_*` for funding facts and other ToolUniverse sources only for downstream evidence they actually cover.
Always read [references/tool-reference.md](references/tool-reference.md) before choosing tools or comparing totals. Read [references/verified-cases.md](references/verified-cases.md) when testing the skill, debugging an unexpected response, or adapting one of the verified case patterns. Read [references/public-value-cases.md](references/public-value-cases.md) for a patient, family, advocate, journalist, trainee, applicant, taxpayer, policy, or other public-facing request.
## Public Value Routing
Start from the reader's decision, not from the available endpoints:
| Reader | Optimize the answer for | Never imply |
|---|---|---|
| Patient, family, advocate | A topic-specific research map, recent activity, inspectable projects, and next contacts | Clinical expertise, quality of care, treatment advice, or patient benefit from funding alone |
| Journalist, taxpayer, policy analyst | A reproducible headline number, its definition, its largest drivers, and its sensitivity to alternate queries | That the largest number is the truest, a partial year is final, or spending caused outcomes |View the complete source on GitHubtooluniverse-noncoding-rna
When your agent uses it
Non-coding RNA analysis — miRNAs (miRBase, miRDB targets), lncRNAs (LNCipedia, RNAcentral), circRNAs, snoRNAs, and other ncRNA classes. Distinct mechanisms per class — miRNAs repress mRNA; lncRNAs scaffold/decoy/enhance. Use for ncRNA function prediction, miRNA-target prediction, lncRNA functional annotation, and ncRNA-disease association queries.
What it loads into context
Frontmatter
111 tokensAlways available to the agent
SKILL.md
4,308 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Core Tools
- Workflow
- Limitations
- Limitations
Files
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# Non-Coding RNA Analysis
Pipeline for identifying, annotating, and interpreting non-coding RNAs and their biological roles. Covers microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and other ncRNA classes.
**Key principles**:
1. **Class determines function** — miRNAs repress mRNA translation; lncRNAs have diverse mechanisms (scaffolds, guides, decoys, enhancers); rRNAs/tRNAs are structural
2. **Targets matter more than the ncRNA itself** — for miRNAs, the regulated mRNA targets determine the phenotype
3. **Expression context is critical** — ncRNAs are highly tissue/cell-type specific
4. **Conservation indicates function** — deeply conserved ncRNAs (miR-let-7, MALAT1) have well-established roles
5. **Evidence grading** — T1: validated targets (reporter assay, CLIP-seq), T2: high-confidence computational prediction, T3: expression correlation, T4: sequence-based prediction only
**Type-based reasoning — look up, don't guess**:
Non-coding RNA function depends on type: miRNA silences target mRNAs (look up targets in miRTarBase/TargetScan), lncRNA has diverse functions (scaffolding, guiding, decoying — check literature for the specific lncRNA), circRNA may sponge miRNAs.
View the complete source on GitHubtooluniverse-nutrition-food-composition
When your agent uses it
Look up nutrient composition, ingredients, allergens, additives, and processing-level classification for foods and branded products via USDA FoodData Central (authoritative US nutrient values, generic + branded foods) and Open Food Facts (crowdsourced global product database — barcode lookup, allergens, additives, Nutri-Score, NOVA processing classification). Use when someone asks 'how much [nutrient] is in [food]', 'what are the ingredients/allergens in [branded product]', 'look up this barcode', 'find products containing/free of [additive/allergen]', or needs dietary/nutrient data for a clinical or research calculation.
What it loads into context
Frontmatter
150 tokensAlways available to the agent
SKILL.md
1,930 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- 1. USDA FoodData Central — Authoritative US Nutrient Values
- 2. Open Food Facts — Crowdsourced Global Product Database
- When to Use Which
- Common Pitfalls
- References
Files
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# Nutrition & Food Composition Lookup
Looks up what's actually in a food or branded product — nutrient values, ingredients, allergens, additives, processing classification — from two complementary sources. Does not itself perform toxicological or epidemiological analysis; feed the retrieved data into `tooluniverse-toxicology` (food additive/allergen safety questions) or `tooluniverse-epidemiological-analysis` (dietary-exposure questions) for that.
**LOOK UP, DON'T GUESS**: Never state a nutrient value, ingredient list, allergen, or Nutri-Score from memory — both sources are live and current; query them.
## When to Use This Skill
- "How much [protein/sodium/vitamin C/...] is in [food]?" -> FoodDataCentral
- "What's the full nutrient profile of [generic or USDA reference food]?" -> FoodDataCentral
- "What are the ingredients/allergens in [branded product]?" -> OpenFoodFacts
- "Look up this barcode" -> `OpenFoodFacts_get_product`
- "Find products with/without [additive, allergen, Nutri-Score grade, label]" -> `OpenFoodFacts_filter_products_by_tags`
View the complete source on GitHubtooluniverse-organic-chemistry
When your agent uses it
Organic chemistry reasoning guide for reaction product prediction, mechanism analysis (electrophilic/nucleophilic substitution, addition, elimination, pericyclic, radical), and spectroscopy interpretation (1H/13C NMR, IR, MS). Reasons from first principles (electron flow, kinetic vs thermodynamic) rather than pattern-matching named reactions. Use for organic synthesis problems and mechanism explanations.
What it loads into context
Frontmatter
97 tokensAlways available to the agent
SKILL.md
5,763 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Predicting Reaction Products — A Reasoning Process
- 2. Spectroscopy Interpretation — A Problem-Solving Strategy
- 3. Stereochemistry — How to Think in 3D
- 4. Bundled Scripts
- 5. Computational Procedures
- 6. When to Reason vs When to Look Things Up
Scripts executed separately
scripts/chemistry_facts.pyscripts/crystal_validator.pyscripts/degrees_of_unsaturation.pyscripts/molecular_complexity.pyscripts/molecular_formula.pyscripts/smiles_verifier.pyscripts/stereochem_tracker.py
Files
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Read SKILL.md
# Organic Chemistry Reasoning Guide
This skill teaches **how to think through** organic chemistry problems, not what to memorize.
---
## 1. Predicting Reaction Products — A Reasoning Process
Do NOT pattern-match to a named reaction. Instead, reason from first principles:
### Step 1 — Find the most reactive sites
Look at each molecule and ask: where is the electron density?
- **Electron-rich sites** (nucleophiles): lone pairs on N, O, S; pi bonds (C=C, aromatics); carbanions; enolatesView the complete source on GitHubtooluniverse-pathway-disease-genetics
When your agent uses it
Connect GWAS variants to biological pathways and druggable targets. Maps GWAS hits to causal genes (via fine-mapping/eQTL), then to pathways (Reactome, KEGG, WikiPathways), then to existing drugs hitting those pathways. Use for pathway-level disease mechanisms, druggable-pathway prioritization from GWAS, SNP-to-pathway-to-target tracing, and tissue-specific eQTL evidence for drug target hypotheses.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
2,475 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Reasoning Principles
- Tool Selection Guide
- Three-Step Workflow
- Key Parameter Gotchas
- Limitations
- Related Skills
Files
SKILL.mdcore10,090 bytes
Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Pathway-Disease Genetics: GWAS to Drug Targets via Pathways
Connect genome-wide association study (GWAS) variants to biological pathways for mechanistic understanding and drug target discovery.
## When to Use
- "What pathways are disrupted in [disease] based on GWAS?"
- "Which GWAS genes for [trait] are in druggable pathways?"
- "Map [SNP/variant] to its causal gene and pathway"
- "Find drug targets from GWAS data for [disease]"
- "What is the eQTL evidence for [gene] in [tissue]?"View the complete source on GitHubtooluniverse-peptide-target-deorphanization
When your agent uses it
Find the real protein target(s) of a peptide from its sequence — peptide target deorphanization / off-target identification, for ANY target class (GPCR, ion channel, protease, cytokine/growth-factor receptor, enzyme, integrin), not only GPCRs. Use when a peptide has a phenotype but does not bind its hypothesized target, when a peptide binds a target in one species or assay but not another, or to screen candidate targets for an orphan peptide. A target-class router steers a multi-route keyless pipeline (PROSITE/ELM motif, BLAST homology, HGNC/InterPro/GPCRdb/GtoPdb target-family enumeration, OpenTargets phenotype anchor, EnsemblCompara/Alliance cross-species reconciliation) plus optional NVIDIA-NIM co-folding (Boltz2, AlphaFold2-Multimer, OpenFold3) for structural confirmation.
What it loads into context
Frontmatter
206 tokensAlways available to the agent
SKILL.md
4,520 tokensWhen the skill is activated
Reference documents
11,637 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core reasoning: LOOK UP, DON'T GUESS
- Automated pipeline (scripts) — the fast path
- The pipeline at a glance
- Output format — ranked shortlist report (return inline, no extra files)
- Peptide Bioactivity Property Databases (supplementary characterization)
- Validation & test set
Scripts executed separately
scripts/cofold_screen.pyscripts/deorphanize_peptide.py
Files
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Read SKILL.md
# Peptide Target Deorphanization
Deorphanize a peptide: given a peptide **sequence** plus an **observed phenotype** (and often a *hypothesized* target the peptide does NOT actually bind), find its likely real protein target(s) — using ToolUniverse's keyless characterization, homology, target-family, phenotype, cross-species, and (optional, key-gated) co-folding tools.
**The target can be anything, not just a GPCR.** A *target-class router* (GPCR ligand / ion-channel toxin / protease target / cytokine or growth-factor receptor / integrin ligand / antimicrobial / unknown) classifies the peptide up front and adapts the enumeration strategy. GPCRs are the best-trodden case (and the validated control), but the pipeline's spine — homology, motif, phenotype, cross-species, co-fold — is target-class-agnostic, and family enumeration uses **HGNC gene-family (general) + InterPro (general) + GPCRdb (GPCR-only cross-check)**.
## Core reasoning: LOOK UP, DON'T GUESS
The failure mode this skill defends against is **guessing a target from the peptide's name or its assumed mechanism**. A peptide can be **phenotypically active yet not bind the hypothesized target** — because it hits a *paralog*, a *different family member*, or the *same receptor in a different species* whose binding interface has diverged. So:
1. **Never assert a target from memory.** Every candidate must come from a tool result (homology hit, family enumeration, phenotype association, or structural co-fold), with the tool name and accession recorded.
2. **Anchor on PHENOTYPE × STRUCTURE/SEQUENCE plausibility, not on the peptide's reputed mechanism.** The real target is the intersection of (a) what the *sequence/motif/structure* says it could bind and (b) what the *phenotype* says is biologically relevant. A name-level guess ("it's a GLP-1 analog so it's GLP1R") is exactly what produces off-target errors.
3. **Reconcile across species.** "Binds in species A but not B" is usually **interface sequence divergence**, not a different target. Always pull the ortholog set and align the candidate receptor's ligand-binding interface across the assay species before concluding the peptide "doesn't work."
4. **A non-binding result against the hypothesized target is a clue, not a dead end.** It promotes the *paralogs* and *phenotype-shared* receptors to the top of the candidate list.View the complete source on GitHubtooluniverse-pharmacogenomics
When your agent uses it
Pharmacogenomics (PGx) research — drug-gene interactions (CPIC, PharmGKB), CPIC dosing guidelines, variant-drug-response associations, ethnic-allele-frequency considerations, and metabolizer-status scoring. Use for PGx-informed dosing recommendations, CYP/HLA pharmacogenomic allele interpretation, and clinically-actionable PGx report generation.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
5,689 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Workflow Overview
- Phase 0: Disambiguation
- Phase 1: Identify Gene-Drug Pairs
- Phase 2: Retrieve Dosing Guidelines
- Phase 3: Allele & Variant Annotation
- Phase 4: FDA Biomarker Labeling
- Phase 5: Cross-Database Enrichment
- When PGx Testing Changes Clinical Decisions
- Key Parameter Notes
- CPIC Guideline Application Reasoning
- Fallback Strategies
- Example Workflows
- Drug Class Context (RxClass)
- FDA Substance Identification (FDAGSRS)
- Limitations
Files
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Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Pharmacogenomics (PGx) Research Skill
Systematic PGx analysis: resolve gene-drug pairs, retrieve CPIC dosing guidelines, annotate alleles and variants with PharmGKB, check FDA PGx biomarker labeling, and generate evidence-graded clinical recommendations.
## When to Use
- "What CPIC guidelines exist for CYP2D6?"
- "Get dosing recommendations for codeine based on CYP2D6 poor metabolizer status"
- "Which drugs have FDA pharmacogenomic biomarkers for CYP2C19?"
- "Find PharmGKB clinical annotations for rs1799853"
- "Is this patient's CYP2D6 genotype relevant to tamoxifen dosing?"View the complete source on GitHubtooluniverse-pharmacokinetics
When your agent uses it
Pharmacokinetic (PK) analysis of concentration-time data — non-compartmental analysis (NCA) for Cmax, Tmax, AUC (0-t and 0-∞), terminal half-life, clearance (CL), volume of distribution (Vd), MRT, and absolute bioavailability (F). Also one-compartment fitting. Use when you have plasma/serum drug concentrations over time after a dose and need PK parameters, or to compute bioavailability from IV + oral AUCs. NOT for ADMET property prediction from structure (use tooluniverse-admet-prediction).
What it loads into context
Frontmatter
140 tokensAlways available to the agent
SKILL.md
1,693 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- Step 1 — Prepare the concentration-time data
- Step 2 — Run NCA
- Step 3 — Interpret the parameters
- Step 4 — Absolute bioavailability (F)
- Step 5 — Quality gotchas (state these)
- Honest limitations
- Related skills
Scripts executed separately
scripts/nca_from_csv.py
Files
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Read SKILL.md
# Pharmacokinetic (PK) Analysis — Non-Compartmental Analysis
Turn a **concentration-vs-time** profile after a dose into the standard PK parameters, and compute **bioavailability** from IV + oral data. Non-compartmental analysis (NCA) is the model-independent workhorse used for most PK reporting.
## When to use this
- You have measured plasma/serum (or other matrix) drug concentrations at known times after a dose.
- You need Cmax/Tmax/AUC/half-life/clearance/Vd, or absolute bioavailability F.
- Comparing exposure (AUC, Cmax) between formulations, doses, or routes.
This is **measured-data** PK. For predicting ADMET properties from a chemical structure, use `tooluniverse-admet-prediction`.
## Step 1 — Prepare the concentration-time data
View the complete source on GitHubtooluniverse-pharmacovigilance
When your agent uses it
Drug safety and adverse event analysis — FAERS spontaneous-report mining, FDA black-box warnings, signal detection (PRR, ROR, IC), risk factors by demographic/comorbidity, label change tracking, and SIDER curated label-derived side-effect frequencies. Use for post-market safety surveillance, AE signal investigation, drug-AE association strength scoring, "what side effects does drug X have" questions, and pharmacovigilance reports.
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
4,704 tokensWhen the skill is activated
Reference documents
15,414 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Clinical Reasoning Framework
- Critical Workflow Requirements
- Tool Parameter Reference (CRITICAL)
- Workflow Overview
- Phase 0: Mechanistic Reasoning (DO THIS BEFORE TOOLS)
- Phase 1: Drug Disambiguation
- Phase 2: Adverse Event Profiling (FAERS)
- Phase 3: Label Warning Extraction
- Phase 4: Pharmacogenomic Risk
- Phase 5: Clinical Trial Safety
- Phase 5.5: Pathway & Mechanism Context
- Phase 5.6: Literature Intelligence
- Phase 6: Signal Prioritization
- Output Report
- Evidence Grading
- Fallback Chains
- Completeness Checklist
- References
Files
CHECKLIST.mddoc6,128 bytesEXAMPLES.mddoc15,683 bytesREPORT_TEMPLATES.mddoc8,302 bytesSIGNAL_ANALYSIS.mddoc6,328 bytesSIGNAL_DETECTION.mddoc2,950 bytesSKILL.mdcore19,467 bytesSKILL.mdcore19,467 bytesSKILL.mdcore19,467 bytesTOOLS_REFERENCE.mddoc15,712 bytes
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Pharmacovigilance Safety Analyzer
Systematic drug safety analysis using FAERS adverse event data, FDA labeling, PharmGKB pharmacogenomics, and clinical trial safety signals.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, update progressively
2. **Signal quantification** - Use disproportionality measures (PRR, ROR)
3. **Severity stratification** - Prioritize serious/fatal events
4. **Multi-source triangulation** - FAERS, labels, trials, literature
5. **Pharmacogenomic context** - Include genetic risk factors
6. **Actionable output** - Risk-benefit summary with recommendationsView the complete source on GitHubtooluniverse-phewas
When your agent uses it
Cross-ancestry / cross-biobank phenome-wide association (PheWAS) and replication. Given ONE variant (rsID) or ONE gene, look up every phenotype it associates with across European/UK (UKB-TOPMed), Finnish (FinnGen), Japanese (BioBank Japan), and Taiwanese (TPMI) biobanks, plus exome-wide gene-burden PheWAS (Genebass), then judge whether an association replicates across ancestries or is population-specific. Use whenever the user asks "what else is this variant/gene associated with", "does this association replicate in other ancestries / biobanks", "is this effect East-Asian-specific", "pleiotropy of rsXXX", "phenome scan", or wants to compare effect sizes/allele frequencies of a variant across populations. NOT for the forward direction (trait → which SNPs: use the gwas-* skills), NOT for fine-mapping a locus (use tooluniverse-gwas-finemapping), and NOT for single-SNP mechanism tracing in one population (use tooluniverse-gwas-snp-interpretation).
What it loads into context
Frontmatter
250 tokensAlways available to the agent
SKILL.md
2,567 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The biobank panel
- Workflow
- Interpretation
- Report structure
- Worked example (rs7903146, TCF7L2)
- Limitations
Files
SKILL.mdcore9,882 bytes
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# Cross-Biobank PheWAS & Replication
A **PheWAS is the inverse of a GWAS**: a GWAS fixes a phenotype and scans variants; a PheWAS fixes a variant (or gene) and scans the entire phenome. The scientific payoff of running it across *several* biobanks is **replication and ancestry resolution** — an association seen in one population that reappears in another (with the same effect direction) is far more credible, while one that appears only in East-Asian cohorts may reflect ancestry-specific LD, allele frequency, or biology.
This skill orchestrates one variant/gene across four ancestries plus a gene-burden layer. **Look up, don't guess** — never assert a variant's phenotype associations or effect sizes from memory; the whole point is the live cross-biobank numbers.
## The biobank panel
| Tool | Biobank | Ancestry | Build | Evidence type |
|---|---|---|---|---|
| `UKBTOPMed_phewas_by_variant` | UKB-TOPMed | European (UK) | GRCh38 | per-phenotype assoc (phecodes, ~1,400) |
| `FinnGen_get_variant_finemapping` | FinnGen | Finnish | GRCh38 | credible-set membership (fine-mapping) |
| `BioBankJapan_phewas_by_variant` | BioBank Japan | Japanese | **GRCh37** | per-phenotype assoc |
| `TPMI_phewas_by_variant` | TPMI | Taiwanese (Han) | GRCh38 | per-phenotype assoc (ICD-based) |View the complete source on GitHubtooluniverse-phylogenetics
When your agent uses it
Phylogenetic analysis — de novo multiple sequence alignment (Clustal Omega/MUSCLE/MAFFT via EBI_msa_align) and neighbour-joining/UPGMA tree building (EBI_build_phylogenetic_tree) from your own sequences, plus tree analysis, treeness, saturation (PhyKIT), parsimony-informative sites, alignment gap analysis, DVMC, long-branch detection, BUSCO orthologs. Uses PhyKIT, Biopython, DendroPy. Use to align a set of sequences, build a tree from sequences or an alignment, or for phylogenetic tree QC, multi-gene phylogenomics, evolutionary-rate analysis, and comparative-genomics studies.
What it loads into context
Frontmatter
167 tokensAlways available to the agent
SKILL.md
9,112 tokensWhen the skill is activated
Reference documents
16,298 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Four traps that produce a confidently wrong number
- RULE ZERO — Check for pre-computed results FIRST
- BUSCO scogs questions (multi-species phylogenomics)
- LOOK UP, DON'T GUESS
- When to Use
- Required Packages
- Workflow Decision Tree
- Quick Reference
- Common Patterns
- Answer Extraction
- Interpretation
- Completeness Checklist
- Analysis conventions
- References
Scripts executed separately
scripts/busco_target_orthologs.pyscripts/format_alignment.pyscripts/phykit_batch.pyscripts/scogs_paired_compare.pyscripts/scogs_phykit_pipeline.pyscripts/tree_statistics.py../../../skills/tooluniverse-phylogenetics/scripts/phykit_batch.py
Files
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Read SKILL.md
# Phylogenetics and Sequence Analysis
## Four traps that produce a confidently wrong number
Each of these was observed producing a wrong answer *while the correct guidance
was already present further down this file*. Check them before you answer.
1. **PhyKIT prints more than one column, and for `saturation` the two
conventions disagree — state which you used.** `phykit saturation` prints
`saturation <TAB> |saturation-1|`. Its own `--help` is explicit: *"The first
value is the saturation value and the second column is the absolute value of
saturation minus 1."* But several published analyses (and some reference
answers derived from them) report the **second** column as "the saturation
value". The two always sum to 1.0000, which is the tell that you may beView the complete source on GitHubtooluniverse-plant-genomics
When your agent uses it
Plant genomics and biology research — PlantReactome pathways, Ensembl Plants gene structure, POWO species taxonomy, UniProt annotation, KEGG plant pathways. Handles polyploidy (wheat hexaploidy etc.) and homeologous gene copies. Use for crop-gene annotation, plant secondary metabolism queries, and plant-disease/stress-response biology.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,995 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- When to Use
- Core Tools
- Workflow
- Limitations
Files
SKILL.mdcore8,235 bytesSKILL.mdcore8,235 bytesSKILL.mdcore8,235 bytesSKILL.mdcore8,235 bytes
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# Plant Genomics & Biology
Pipeline for investigating plant genes, metabolic pathways, species taxonomy, and comparative plant biology using ToolUniverse tools.
## Reasoning Strategy
Plant genomes are large (wheat is ~17 Gb, vs. 3 Gb for human) and often polyploid — wheat is hexaploid (AABBDD), meaning there are three homeologous copies of most genes. When comparing plant genes to Arabidopsis, always account for whole-genome duplications: a single Arabidopsis gene may have 2–4 paralogs in a crop species, all potentially with diverged functions. Gene families are massively expanded in plants relative to animals (e.g., receptor-like kinases, cytochrome P450s, transcription factors) — a BLAST hit does not mean functional equivalence. Arabidopsis thaliana is the primary model, but its small genome and rapid life cycle mean some features (wood formation, nitrogen fixation symbiosis, C4 photosynthesis) are absent and must be studied in other species.
**LOOK UP DON'T GUESS**: Do not assume gene function by sequence similarity alone in polyploid species; look up functional validation evidence via UniProt (reviewed entries) or PlantReactome. Do not assume KEGG organism codes — use the table or query `kegg_search_pathway` with the species name to confirm availability.
**Key principles**:
1. **Plant-specific pathways** — photosynthesis, secondary metabolism, hormone signaling are unique to plants
2. **PlantReactome as foundation** — curated plant pathway database with cross-species coverage (Oryza, Arabidopsis, Zea mays, etc.)
3. **Ensembl Plants for genomics** — use Ensembl with plant species names for gene lookup and annotationView the complete source on GitHubtooluniverse-polygenic-risk-score
When your agent uses it
Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Covers PRS construction (clumping/thresholding, PRS-CS), validation in independent cohorts, ancestry-aware adjustment, and clinical interpretation (population-relative risk, not absolute prediction). Use for PRS-based risk stratification.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
3,953 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- Overview
- Methodology
- Data Sources
- Key Concepts
- Applications
- Limitations and Considerations
- Workflow
- Best Practices
- Disclaimer
Scripts executed separately
check_data.pypython_implementation.py
Files
.env.templateasset499 bytesSKILL.mdcore17,094 bytesSKILL.mdcore17,094 bytesSKILL.mdcore17,094 bytesSKILL.mdcore17,094 bytesSKILL.mdcore17,094 bytescheck_data.pyruns278 bytespython_implementation.pyruns16,790 bytes
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# Polygenic Risk Score (PRS) Builder
Build and interpret polygenic risk scores for complex diseases using genome-wide association study (GWAS) data.
## Reasoning Strategy
A polygenic risk score predicts genetic risk, not disease. A high PRS means elevated risk relative to the population — it does not mean the person will develop the condition, and a low PRS does not confer immunity. PRS performance varies dramatically across ancestries: a European-derived PRS applied to a West African population can lose 50–70% of its predictive power because the underlying GWAS was trained on European allele frequencies and LD patterns. Effect sizes from discovery GWAS are subject to winner's curse (overestimation in single studies); always prefer weights from large meta-analyses or validated PGS Catalog models. PRS should always be interpreted in the context of non-genetic risk factors — for most complex diseases, environmental factors contribute as much or more than genetics.
**LOOK UP DON'T GUESS**: Do not assume effect sizes, allele frequencies, or which SNPs are genome-wide significant for a trait — always query GWAS Catalog (`gwas_get_associations_for_trait`) for actual data. Do not assume a validated PRS model exists for a trait; check PGS Catalog via PubMed search.
## Overview
**Use Cases:**
- "Calculate my genetic risk for type 2 diabetes"View the complete source on GitHubtooluniverse-population-genetics
When your agent uses it
Population genetics analysis — allele frequencies (gnomAD, 1000 Genomes), Hardy-Weinberg equilibrium testing, Fst between populations, GWAS associations, evolutionary constraint scores. Use for cross-population variant comparison, ancestry-aware allele frequency lookups, and population-level evolutionary analysis.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
5,584 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- LOOK UP, DON'T GUESS
- Tool Quick Reference
- Workflow Patterns
- Theoretical Reasoning (CRITICAL for computation problems)
- Mendelian Genetics Reasoning Framework
- E. coli Hfr Mapping Framework
- MCQ Elimination Strategy for Genetics
- Common Genetics Reasoning Traps
- Advanced Genetics Traps v2
- Bundled Computation Script
- Key Concepts
- Evidence Grading
Scripts executed separately
scripts/popgen_calculator.py
Files
SKILL.mdcore21,630 bytesSKILL.mdcore21,630 bytesscripts/popgen_calculator.pyruns17,228 bytes
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# Population Genetics Analysis
**MC Strategy**: Population genetics MC questions often test whether you know a specific theorem or result. COMPUTE the answer first (use popgen_calculator.py or write Python), then match to options. Don't try to reason about which option "sounds right."
Analyze population-level genetic variation, allele frequencies, GWAS associations, clinical significance, and evolutionary constraints using ToolUniverse tools.
## When to Use
Activate this skill when the user asks about:
- Allele frequencies across populations (gnomAD, 1000 Genomes)
- GWAS associations for diseases/traits
- Clinical variant interpretation (ClinVar, VEP)
- Gene-level constraint metrics (pLI, LOEUF, o/e ratios)
- Selection, drift, linkage disequilibrium, or population structureView the complete source on GitHubtooluniverse-population-genetics-1000genomes
When your agent uses it
Population genetics using the 1000 Genomes Project (IGSR) — superpopulation/population search, sample metadata, variant frequencies across AFR/AMR/EAS/EUR/SAS, ancestry-specific analyses. Use for ancestry comparison, population-aware allele frequency lookups, and 1000-Genomes-cohort-specific analyses (distinct from gnomAD which has different sample composition).
What it loads into context
Frontmatter
191 tokensAlways available to the agent
SKILL.md
2,788 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- NOT for (use other skills instead)
- Phase 1: Search 1000 Genomes Populations
- Phase 2: Search Samples by Population
- Phase 3: List Data Collections
- Phase 4: GWAS Context for Population Stratification
- Workflow: Population Stratification in GWAS
- Common Population Codes
- Reasoning Framework for Result Interpretation
- Tool Parameter Quick Reference
Files
SKILL.mdcore10,796 bytes
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Population Genetics with 1000 Genomes (IGSR)
Use IGSR tools to search 1000 Genomes populations and samples, explore data collections, and
combine with GWAS tools for population-stratified analysis.
## When to Use
- "List all African (AFR) populations in the 1000 Genomes Project"
- "Find samples from the YRI (Yoruba) population"
- "What 1000 Genomes data collections are available?"
- "Which GWAS SNPs for type 2 diabetes have population-specific effects?"View the complete source on GitHubtooluniverse-precision-medicine-stratification
When your agent uses it
Patient stratification for precision medicine — integrate genomic, clinical, and therapeutic data to split patients into responder/non-responder groups, risk tiers, or treatment-decision groups. Use for stratification-by-biomarker, treatment-selection logic, and personalized therapeutic strategy reports per patient subgroup.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
2,826 tokensWhen the skill is activated
Reference documents
10,057 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parsing
- Critical Tool Parameter Notes
- Workflow Overview
- Phase 1: Disease Disambiguation & Profile Standardization
- Phase 2: Genetic Risk Assessment
- Phase 3: Disease-Specific Molecular Stratification
- Phase 4: Pharmacogenomic Profiling
- Phase 5: Comorbidity & Drug Interaction Risk
- Phase 6: Molecular Pathway Analysis
- Phase 7: Clinical Evidence & Guidelines
- Phase 8: Clinical Trial Matching
- Phase 9: Integrated Scoring & Recommendations
- Common Use Patterns
Files
.env.templateasset499 bytesEXAMPLES.mddoc8,656 bytesQUICK_START.mddoc3,691 bytesREPORT_TEMPLATE.mddoc5,134 bytesSCORING_REFERENCE.mddoc9,471 bytesSKILL.mdcore10,805 bytesSKILL.mdcore10,805 bytesTOOLS_REFERENCE.mddoc8,727 bytes
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# Precision Medicine Patient Stratification
Transform patient genomic and clinical profiles into actionable risk stratification, treatment recommendations, and personalized therapeutic strategies.
## Reasoning Before Searching
Stratification means splitting patients into groups that respond differently to a treatment or have different prognoses. Ask these questions before running any tools:
1. **What molecular feature predicts response?** Candidates: somatic mutation (e.g., EGFR L858R), germline variant (e.g., BRCA1 LoF), expression level (e.g., HER2 overexpression), germline pharmacogenomic variant (e.g., CYP2C19 PM), or composite biomarker (e.g., TMB-H + MSI-H).
2. **Is the predictive feature actionable?** Knowing it must change treatment — either the drug choice, dose, or monitoring plan. A variant with prognostic value but no therapeutic consequence is not a stratification biomarker.
3. **What is the evidence level for the stratifier?** FDA-approved companion diagnostic (T1) vs. exploratory (T4) changes how much weight to place on the finding.
Route to the correct Phase 3 path BEFORE running Phase 2 tools — cancer, metabolic, CVD, rare disease, and autoimmune pipelines require different stratifiers.
View the complete source on GitHubtooluniverse-precision-oncology
When your agent uses it
Cancer treatment recommendations from molecular profile (mutations + cancer type + biomarkers) — FDA-approved + investigational therapies, resistance mechanisms, matching clinical trials, prognosis. Uses CIViC, ClinVar, OpenTargets, ClinicalTrials.gov. Use for tumor-board treatment recommendations, evidence-tiered actionability assessment, and FDA-precedent-driven therapy selection.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
2,967 tokensWhen the skill is activated
Reference documents
16,613 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- Treatment Selection Reasoning
- LOOK UP DON'T GUESS
- When to Use
- Phase 0: Tool Verification
- Workflow Overview
- Key Tools by Phase
- Cross-Skill References
- References
Scripts executed separately
scripts/gdsc_drug_response.py../../../skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py
Files
API_USAGE_PATTERNS.mddoc18,745 bytesCHECKLIST.mddoc3,104 bytesEXAMPLES.mddoc11,413 bytesREPORT_TEMPLATE.mddoc6,046 bytesSKILL.mdcore11,176 bytesTOOLS_REFERENCE.mddoc19,810 bytesTREATMENT_ALGORITHMS.mddoc2,950 bytesscripts/gdsc_drug_response.pyruns4,653 bytes../../../skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.pyruns36,294 bytes
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# Precision Oncology Treatment Advisor
Provide actionable treatment recommendations for cancer patients based on their molecular profile using CIViC, ClinVar, OpenTargets, ClinicalTrials.gov, and structure-based analysis.
## Domain Reasoning
Treatment selection follows a strict evidence hierarchy: FDA-approved for this specific mutation in this cancer type ranks highest, followed by approval for this mutation in any cancer (tumor-agnostic), then active clinical trials, and finally off-label use. Skipping this hierarchy to recommend off-label therapies when an approved option exists is a clinical error. Always check current NCCN guidelines and recent literature, as approvals change rapidly — a drug that was investigational last year may now be first-line.
When looking up treatment for a specific mutation, search CIViC and OncoKB FIRST, not PubMed. These databases have curated evidence levels. PubMed is for when curated databases don't have the answer.
## Treatment Selection Reasoning
**Biomarker-to-drug logic** — When a biomarker is identified, the first-line targeted therapy follows established mappings. Always verify current approval status via OncoKB/CIViC, but use this as a starting framework:
- **NSCLC**: EGFR exon 19 del / L858R → osimertinib (1L); ALK fusion → alectinib/lorlatinib; ROS1 fusion → crizotinib/entrectinib; KRAS G12C → sotorasib/adagrasib; MET exon 14 skip → capmatinib/tepotinib; RET fusion → selpercatinib; BRAF V600E → dabrafenib+trametinib; NTRK fusion → larotrectinib/entrectinib (tumor-agnostic)View the complete source on GitHubtooluniverse-primer-design
When your agent uses it
PCR / qPCR primer and oligo design — design forward/reverse primers for a target region (SantaLucia nearest-neighbor thermodynamics), compute melting temperature (Tm) and annealing temperature (Ta), check GC content, and screen an oligo for hairpins and primer-dimers. Use when you need primers for a sequence, want to QC an existing primer pair, or need the Tm of an oligo. Covers the primer-design rules (Tm matching, GC clamp, 3'-end, length) and the tools' constraint quirks.
What it loads into context
Frontmatter
131 tokensAlways available to the agent
SKILL.md
1,483 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- Step 1 — Design a primer pair
- Step 2 — Get Tm / annealing temperature for specific primers
- Step 3 — Primer design rules (what "good" looks like)
- Step 4 — Specificity (the tools do NOT do this)
- Step 5 — Common gotchas
- Honest limitations
- Related skills
Scripts executed separately
scripts/primer_qc.py
Files
SKILL.mdcore5,788 bytesSKILL.mdcore5,788 bytesSKILL.mdcore5,788 bytesscripts/primer_qc.pyruns3,808 bytes
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# PCR / qPCR Primer & Oligo Design
Design primers for a target DNA region, get their Tm/Ta, and QC them for the secondary-structure problems that make a PCR fail.
## When to use this
- Design a forward/reverse primer pair to amplify a region of a sequence.
- Compute the Tm / annealing temperature of a primer.
- QC an existing primer pair (GC clamp, 3'-end, hairpins, self/cross dimers, Tm match).
## Step 1 — Design a primer pair
```bash
tu run DNA_primer_design '{"operation":"primer_design",View the complete source on GitHubtooluniverse-product-safety-surveillance
When your agent uses it
Post-market safety surveillance and recall/adverse-event RETRIEVAL across the full spectrum of FDA-regulated products that are NOT covered by the drug-AE signal skills: medical devices, food / dietary supplements / cosmetics, veterinary drugs, and drug supply (shortages). Orchestrates openFDA endpoints (MAUDE device adverse events + device recalls + 510(k), CAERS food/supplement/ cosmetic adverse events, veterinary adverse events, drug shortages, and cross-product enforcement/recall reports). USE WHEN the user asks: "are there adverse events for [device / pacemaker / infusion pump / insulin pump]", "device recalls for [firm/product]", "supplement / vitamin / cosmetic adverse reactions", "is [drug] in shortage", "what injectables are on shortage", "veterinary / animal adverse events for [drug] in [dog/cat/horse]", "food recall for listeria", "MAUDE report for [device]", "CAERS reactions for [brand]". DO NOT USE for drug adverse-event SIGNAL detection or disproportionality (PRR / ROR / IC) or drug-AE association scoring — that is `tooluniverse-pharmacovigilance` / `tooluniverse-adverse-event-detection`. This skill is multi-product surveillance and retrieval, not drug-AE statistical signal mining.
What it loads into context
Frontmatter
325 tokensAlways available to the agent
SKILL.md
4,248 tokensWhen the skill is activated
Reference documents
1,550 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use vs When NOT to Use
- Tool Map (which tool for which question)
- openFDA Query Grammar (CRITICAL — read before querying)
- Interpretation Tables (raw openFDA field → meaning)
- Workflow
- Worked Examples (verified live)
- Limitations (state these in every report)
- References
Files
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# Product Safety Surveillance (multi-product, openFDA)
Retrieve and interpret post-market safety records across **every FDA-regulated
product class except drug-AE signal mining**: medical devices, food / dietary
supplements / cosmetics, veterinary drugs, and drug supply (shortages), plus
cross-product enforcement/recall reports.
**KEY PRINCIPLES**
1. **Decide the product class first.** Device? Food/supplement/cosmetic? Vet drug? Drug shortage? Recall? The class picks the tool.
2. **Build a valid Lucene query.** openFDA uses field-scoped `field:value` terms; combine with a space-separated `AND`. Phrases and special characters need care (see Query Grammar).
3. **Retrieve, then interpret.** These are spontaneous/voluntary reports. Report the records and their fields; never assert causation or rates.
4. **Cite every record** with the tool name, the openFDA endpoint, the query used, and the `total` hit count from `meta.results.total`.
5. **Stay in scope.** If the request is drug-AE signal detection (PRR/ROR/IC), STOP and point to `tooluniverse-pharmacovigilance` / `tooluniverse-adverse-event-detection`.
View the complete source on GitHubtooluniverse-protein-design-campaign
When your agent uses it
Run and review iterative protein binder design campaigns with multiple model families, structural and glycan checks, conditional pH hypotheses, resource-aware computation, and competition submission preparation. Use for ongoing candidate optimization or interpreting conflicting design evidence; not for simple structure retrieval or measured assay analysis.
What it loads into context
Frontmatter
68 tokensAlways available to the agent
SKILL.md
1,293 tokensWhen the skill is activated
Reference documents
4,457 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Establish the current decision
- Choose the next informative computation
- Separate evidence dimensions
- Maintain recoverable evidence
- Prepare a reviewed submission
- Completion criteria
Scripts executed separately
scripts/preflight_submission.py
Files
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Read SKILL.md
# Protein Design Campaign
Turn an ongoing binder-design effort into reproducible candidate decisions and an honestly described experimental or competition submission. Preserve the user's chosen models, infrastructure and goals; this skill does not grant permission to upload unpublished designs, publish a repository or accept terms.
## Establish the current decision
For an existing campaign, read its latest status, manifests, executed results and failure records before launching jobs. Distinguish a completed process from an active one; an old PID or a queued browser file is not proof of ongoing computation or submission.
Record target isoform/species, assay construct and termini, intended epitope, glycosylation assumptions, molecule class, designable positions, and objective direction. Explicitly distinguish **stronger binding at acidic pH** from **binding at neutral pH with acidic release**. They are different objectives, not interchangeable consequences of adding histidine.
For competitions, verify the live challenge, account track, deadlines, novelty definition and molecule formats. Convert the deadline with an explicit UTC offset; AoE means UTC−12. Keep current competition limits in the campaign record, not as permanent defaults in this skill.
## Choose the next informative computation
View the complete source on GitHubtooluniverse-protein-interactions
When your agent uses it
Protein-protein interaction (PPI) network analysis — STRING (predicted + experimental), BioGRID (curated), SASBDB (small-angle scattering), Complex Portal (curated complex membership and stoichiometry, includes CORUM data). Distinguishes physical interactions (binding) from functional associations (co-expression, co-regulation). Use for interactome queries, complex partner identification, curated complex composition lookup, and pathway-level interaction analysis.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
4,225 tokensWhen the skill is activated
Reference documents
13,976 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning: Interaction Type Clarification
- Databases Used
- 4-Phase Workflow
- Complex Portal: Curated Complex Membership
- Parameters
- Network Edge Fields (STRING)
- Extended Analysis Tools
- Tool-Specific Notes
- Domain Reasoning: Multimeric Assemblies & Binding Valency
- Domain Reasoning: Coiled-Coil Oligomeric State Prediction
- Domain Reasoning: Detergent Effects on Membrane Proteins
- Protein Identification Questions
- Troubleshooting
- References
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesDESCRIPTION_UPDATE_COMPLETE.mddoc1,945 bytesDESCRIPTION_UPDATE_PROGRESS.mddoc5,084 bytesDOMAIN_ANALYSIS.mddoc13,105 bytesKNOWN_ISSUES.mddoc3,505 bytesPHASE2_DISCOVERIES.mddoc7,372 bytesQUICK_START.mddoc4,101 bytesSKILL.mdcore17,272 bytesSKILL.mdcore17,272 bytesSKILL.mdcore17,272 bytesSKILL.mdcore17,272 bytesTOOL_DESCRIPTION_OPTIMIZATION.mddoc22,101 bytespython_implementation.pyruns14,741 bytes
Read SKILL.md
# Protein Interaction Network Analysis
Comprehensive protein interaction network analysis using ToolUniverse tools. Analyzes protein networks through a 4-phase workflow: identifier mapping, network retrieval, enrichment analysis, and optional structural data.
## Domain Reasoning: Interaction Type Clarification
When asked about protein interactions, ask: physical interaction (do they bind?) or functional interaction (do they affect the same pathway)? STRING combines both — a high combined_score does not mean physical binding. For physical binding evidence, check the experimental score (`escore`) specifically. A high `tscore` (text mining) or `dscore` (database) with a low `escore` suggests co-annotation or co-citation, not direct binding.
LOOK UP DON'T GUESS: protein interaction scores, experimental evidence types, and whether two specific proteins have known co-crystal structures. Use STRING `escore` and BioGRID experimental data — do not infer binding from pathway co-membership alone.
## Databases Used
| Database | Coverage | API Key | Purpose |
|----------|----------|---------|---------|View the complete source on GitHubtooluniverse-protein-lof-mechanism
When your agent uses it
Propose the mechanism by which a missense variant causes loss-of-function (LoF), synthesizing evidence from 5 independent layers: AlphaMissense pathogenicity, AlphaFold structural context, ESMC sequence likelihood, SAE feature disruption, and DynaMut2 stability ΔΔG. Distinguishes 'structural stability LoF' (mis-folding) from 'direct functional disruption' (catalytic / binding / PTM site damage). Use for coding missense variants where you need a mechanistic causal model, not just a pathogenicity score.
What it loads into context
Frontmatter
136 tokensAlways available to the agent
SKILL.md
3,522 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Required inputs
- Prerequisites
- Workflow
- Reporting format
- Honest limitations
- Optional: ThermoMPNN instead of DynaMut2 (advanced users with GPU)
Files
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# Protein LoF Mechanism Synthesis
For a single missense variant, integrate 5 independent computational signals to propose a specific loss-of-function mechanism. Each signal answers a different question:
| Signal | Tool | Answers |
|---|---|---|
| Pathogenicity | `AlphaMissense_get_variant_score` | "Is this variant damaging?" |
| Structural context | `alphafold_get_prediction` | "Is the mutation in a folded vs disordered region?" |
| Sequence likelihood | `ESM_score_sequence` | "Is the substitution evolutionarily plausible?" |
| Feature disruption | `ESM_explain_variant_mechanism` (or `ESM_score_variant_sae_disruption` + `ESM_describe_sae_feature` for raw control) | "Which biological feature breaks?" |
| Stability | `DynaMut2_predict_stability` | "Does the protein still fold correctly?" |
---
View the complete source on GitHubtooluniverse-protein-modification-analysis
When your agent uses it
Post-translational modification (PTM) analysis — phosphorylation, ubiquitination, acetylation, glycosylation, methylation. Uses iPTMnet (sites + enzymes), ProtVar (functional consequences), UniProt (baseline), STRING, ELM (linear motifs), MassIVE/ProteomeXchange (experimental), and the EBI Proteins API (mutagenesis, MS-proteomics/HPP peptide evidence, antigenic regions, genomic coordinates, protein-level variant lookup by accession/dbSNP/HGVS) as a cross-validation source. Use for PTM site annotation, kinase-substrate identification, and PTM-disease associations.
What it loads into context
Frontmatter
153 tokensAlways available to the agent
SKILL.md
4,101 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Domain Reasoning
- KEY PRINCIPLES
- Workflow
- Phase 0: Disambiguation
- Phase 1: PTM Sites
- Phase 2: Proteoforms
- Phase 3: PTM-Dependent Interactions
- Phase 4: Functional Context
- Phase 4b: Linear Motifs (ELM)
- Phase 4c: Experimental Data
- Phase 4d: EBI Proteins Cross-Validation (Structural, Antigenic, MS-Peptide Evidence)
- Evidence Grading
- Tool Parameter Reference
- Fallbacks
- Limitations
Files
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# Protein Post-Translational Modification Analysis
Comprehensive PTM analysis using iPTMnet (primary), ProtVar (functional context), UniProt (baseline), STRING (interactions), ELM (linear motifs), and MassIVE/ProteomeXchange (experimental data).
> **iPTMnet is down (checked 2026-10-08).** Its REST API has answered HTTP 503 to every request since at least 2026-09-20 while the website stays up, so every `iPTMnet_*` call below currently returns that error. Try one call; if it is a 503, use the fallbacks given in each phase -- `UniProt_get_ptm_processing_by_accession` for modified residues (with the modifying enzyme where curated), `OmniPath_get_enzyme_substrate` for enzyme-substrate pairs, `EBIProteins_get_proteomics_ptm` for mass-spectrometry evidence. Nothing replaces iPTMnet's proteoform and PTM-dependent-interaction data; say so in the report rather than filling the gap.
## LOOK UP DON'T GUESS
- PTM sites/enzymes: `iPTMnet_get_ptm_sites`
- Functional consequence: `ProtVar_get_function` + `iPTMnet_get_ptm_ppi`
- Proteoforms: `iPTMnet_get_proteoforms`
- Linear motifs: `ELM_get_instances`
## COMPUTE, DON'T DESCRIBEView the complete source on GitHubtooluniverse-protein-sae-variant-interpretation
When your agent uses it
Interpret a missense variant via ESMC-6B Sparse Autoencoder (SAE) feature activations. For a given protein + variant, computes which interpretable SAE features (catalytic, ligand-binding, PTM, structural motif, domain, etc.) are lost or gained at the mutation site. Use when standard pathogenicity scores (AlphaMissense, ClinVar) say a variant is damaging but you need a MECHANISTIC explanation — e.g. 'why is this variant LoF?' Complements (does not replace) variant-interpretation and variant-to-mechanism skills, which focus on ACMG classification or regulatory mechanism.
What it loads into context
Frontmatter
156 tokensAlways available to the agent
SKILL.md
3,107 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Required inputs
- Prerequisites
- Workflow (5 steps)
- Interpretation table
- Honest limitations
- Cross-validation pattern (recommended)
- Reporting format
Files
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Read SKILL.md
# Protein SAE Variant Interpretation
Interpret a single missense variant by comparing reference vs mutant Sparse Autoencoder (SAE) feature activations from the ESMC-6B protein language model. SAE features are interpretable latent dimensions of the model's hidden state — many activate on biologically meaningful patterns (active sites, ligand-binding pockets, PTM sequons, structural motifs).
---
## When to use this skill
Apply when users:
- Ask "why is variant X (a missense) loss-of-function?" and need a mechanistic answer beyond a pathogenicity score
- Have an AlphaMissense / ClinVar "damaging" variant and want to know **which functional feature breaks** (catalytic? binding? PTM site? structural?)
- Want to compare ref vs mutant protein representation at a specific residue
- Are interpreting why a structurally subtle change (single AA) has a big functional impact
View the complete source on GitHubtooluniverse-protein-structural-annotation-pdb
When your agent uses it
Given a PDB structure, produce a per-residue annotation table: which residues sit at a binding interface (vs a partner chain), which line a ligand pocket, which are buried (core) vs solvent-exposed (surface), and optionally secondary structure. This is the structural track drawn under a DMS heatmap and the structural prior SAE feature drops are read against. Use when you need to anchor a variant-interpretation or DMS analysis to the protein's actual physical context.
What it loads into context
Frontmatter
122 tokensAlways available to the agent
SKILL.md
1,978 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Required inputs
- Workflow
- Interpretation table
- Honest limitations
- Cross-references
Files
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# Protein structural annotation from a PDB
For each residue of a target protein chain, classify whether it sits at a
binding interface, in a ligand pocket, is buried vs solvent-exposed, and
(optionally) which secondary-structure element it belongs to. This is the
annotation track that anchors any DMS heatmap or per-residue interpretation
to the protein's actual physical context.
---
## When to use this skill
- Building a per-residue annotation track for a DMS heatmap
- Deciding whether a variant of interest is **in an interface, in a pocket, orView the complete source on GitHubtooluniverse-protein-structure-prediction
When your agent uses it
Protein 3D structure prediction from sequence — ESMFold de novo prediction, AlphaFold database retrieval, experimental structures from RCSB, ProtVar variant impact assessment, ProtParam sequence properties. Use for structure prediction when no experimental structure exists, fold-confidence scoring, and structure-guided variant interpretation.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
4,391 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parameters
- Workflow Overview
- Phase 0: Input Preparation
- Phase 1: Sequence Properties
- Phase 2: De Novo Structure Prediction (ESMFold)
- Phase 3: AlphaFold Reference Model
- Complex / Ligand-Binding Prediction (Boltz, paid -- optional)
- Membrane Topology & Signal Peptide Prediction (DTU tools, optional)
- Phase 4: Experimental Structure Comparison
- Phase 5: Variant Structural Impact (When Variant Provided)
- Phase 6: Quality Synthesis and Report
- Tool Parameter Reference
- Fallback Strategies
- Databases Integrated
- Limitations
Files
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# Protein Structure Prediction and Analysis
End-to-end workflow for protein structure prediction starting from a sequence or UniProt accession. Combines ESMFold de novo prediction, AlphaFold database retrieval, experimental structure benchmarking from RCSB, ProtVar variant impact assessment, and ProtParam sequence property calculation.
**KEY PRINCIPLES**:
1. **Sequence first** — obtain or verify the protein sequence before prediction
2. **ESMFold for fast de novo** — works directly on sequence (up to ~800 residues); no database lookup needed
3. **AlphaFold for reference** — retrieve precomputed AlphaFold model for comparison; use `qualifier` parameter (UniProt accession)
4. **Quality before interpretation** — always report pLDDT scores; do not interpret low-confidence regions as folded
5. **Experimental validation** — compare predictions to RCSB experimental structures when available
6. **ProtVar for variants** — use when the question involves mutations or SNVs affecting structure
7. **English-first queries** — use English protein names in all tool calls; respond in the user's language
## LOOK UP, DON'T GUESSView the complete source on GitHubtooluniverse-protein-structure-retrieval
When your agent uses it
Protein structure retrieval from RCSB PDB, PDBe, AlphaFold, SWISS-MODEL, 3D-Beacons (cross-provider structure aggregator), and PDB-REDO (re-refined X-ray structures), with disambiguation, quality assessment (resolution, R-factor, pLDDT, coverage), and metadata. Distinguishes high-quality experimental (X-ray under 2 Angstrom) vs predicted vs homology-model vs medium-quality structures. Use for fetching protein structures, structure-quality comparison, batch model-availability checks across a gene list, checking every structure provider at once, and selecting structures for drug design or modeling.
What it loads into context
Frontmatter
154 tokensAlways available to the agent
SKILL.md
3,198 tokensWhen the skill is activated
Reference documents
2,494 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- Workflow
- Phase 0: Clarification (When Needed)
- Phase 1: Protein Disambiguation
- Phase 2: Data Retrieval (Internal)
- Phase 3: Report Structure Profile
- Quality Assessment
- Error Handling
- Tool Reference
Files
CHECKLIST.mddoc1,647 bytesSKILL.mdcore12,554 bytesSKILL.mdcore12,554 bytesSKILL.mdcore12,554 bytesSKILL.mdcore12,554 bytesexamples.mddoc7,535 bytes
Read SKILL.md
# Protein Structure Data Retrieval
Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.
**IMPORTANT**: Always use English terms in tool calls. Respond in the user's language.
**LOOK UP DON'T GUESS**: Never assume PDB IDs, resolution, or availability. Always query RCSB/PDBe and AlphaFold to confirm.
## Domain Reasoning
Not all structures are equal. X-ray <2 A is high-quality for drug design. Cryo-EM 3-4 A is good for fold but not side chains. AlphaFold is excellent for well-folded domains but unreliable for disordered regions. Always check pLDDT (AlphaFold) or resolution (experimental) before drawing conclusions.
## Workflow
View the complete source on GitHubtooluniverse-protein-therapeutic-design
When your agent uses it
AI-guided de novo protein design — RFdiffusion backbone generation, ProteinMPNN sequence design, structure validation (pLDDT, pTM, MPNN scores). Use for designing therapeutic protein binders, novel scaffolds, enzyme variants, and miniprotein/protein-interface design before experimental validation.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
1,800 tokensWhen the skill is activated
Reference documents
13,317 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Workflow Overview
- Critical Requirements
- NVIDIA NIM Tools
- Supporting Tools
- Folding and Developability Screening
- Completeness Checklist
- Reference Files
Files
../tooluniverse-protein-design-campaign/SKILL.mddoc7,181 bytesCHECKLIST.mddoc6,775 bytesDESIGN_PROCEDURES.mddoc4,117 bytesEXAMPLES.mddoc10,976 bytesSKILL.mdcore7,423 bytesTOOLS_REFERENCE.mddoc16,043 bytesdesign_templates.mddoc2,906 bytes
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# Therapeutic Protein Designer
AI-guided de novo protein design using RFdiffusion backbone generation, ProteinMPNN sequence optimization, and structure validation for therapeutic protein development.
**KEY PRINCIPLES**:
1. **Structure-first** - Generate backbone geometry before sequence
2. **Target-guided** - Design binders with target structure in mind
3. **Iterative validation** - Predict structure to validate designs
4. **Developability-aware** - Consider aggregation, immunogenicity, expression
5. **Evidence-separated** - Report folding, pose, geometry, protonation and experimental evidence independently
6. **Actionable output** - Provide sequences ready for experimental testing
7. **English-first queries** - Always use English terms in tool calls
Therapeutic protein design starts with the target interaction. What binding surface do you need to cover? A small pocket = nanobody or peptide. A large flat surface = designed protein. Stability, immunogenicity, and manufacturability constrain the design space.View the complete source on GitHubtooluniverse-proteomics-analysis
When your agent uses it
Mass-spec proteomics analysis — protein identification, quantification (LFQ, TMT, iTRAQ), differential expression (tumor vs normal, treatment vs control), PTM identification, and pathway enrichment on protein lists. Use when you have proteomics MS output, asking about protein abundance differences, or doing systems-level proteomic interpretation.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
2,976 tokensWhen the skill is activated
Reference documents
3,812 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- RULE ZERO — Check for pre-computed results FIRST
- When to Use This Skill
- COMPUTE, DON'T DESCRIBE
- Core Capabilities
- Workflow Overview
- Integration with ToolUniverse
- Normal-Tissue Expression Context (ProteomicsDB)
- Quantified Minimums
- Interpretation Framework
- Limitations
- References
- Reference Files
Files
CODE_REFERENCE.mddoc8,865 bytesPHASE_DETAILS.mddoc3,317 bytesREPORT_TEMPLATE.mddoc2,648 bytesSKILL.mdcore12,495 bytesSKILL.mdcore12,495 bytes
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# Proteomics Analysis
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
---
Comprehensive analysis of mass spectrometry-based proteomics data from protein identification through quantification, differential expression, post-translational modifications, and systems-level interpretation.View the complete source on GitHubtooluniverse-proteomics-data-retrieval
When your agent uses it
Find and retrieve proteomics datasets from MassIVE, ProteomeXchange, PRIDE Archive, and the NCI Proteomics Data Commons (PDC). Search by species, keyword, accession, or protein; retrieve detailed metadata (instruments, publications, species, PTMs studied), real file-download URLs (PRIDE), or quantitative abundance matrices with clinical linkage (PDC/CPTAC). Use for locating public proteomics datasets to reanalyze, comparing instrument/protocol coverage across studies, pre-download dataset evaluation, and finding every dataset that identified a specific protein.
What it loads into context
Frontmatter
136 tokensAlways available to the agent
SKILL.md
5,199 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- COMPUTE, DON'T DESCRIBE
- KEY PRINCIPLES
- Domain Reasoning: Dataset Quality Assessment
- Core Repositories Integrated
- PeptideAtlas: Peptide-Level Observation Evidence
- Cancer Proteomics via PDC
- PRIDE Archive: Full-Text Search, File Downloads, and Reverse Protein Lookup
- Workflow Overview
- Phase 0: Input Resolution
- Phase 1: Repository Search
- Phase 2: Dataset Detail Retrieval
- Phase 3: Result Synthesis
- Tool Parameter Reference
- Fallback Strategies
- Common Species Taxonomy IDs
- Interpretation Framework
- Limitations
- Integration with Other Skills
- References
Files
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# Proteomics Data Retrieval
Find and retrieve metadata for publicly available proteomics datasets from MassIVE and ProteomeXchange
repositories. Supports searching by species, keyword, or accession, and returns detailed dataset metadata
including instruments, publications, species, and post-translational modifications.
## When to Use This Skill
**Triggers**:
- "Find proteomics datasets for [organism/disease/protein]"
- "Search MassIVE for [keyword]"
- "Get details for PXD000001" or "Look up MSV000079514"
- "What public mass spectrometry datasets exist for [topic]?"
- "Find MS datasets with [PTM type] data"View the complete source on GitHubtooluniverse-publish-tool
When your agent uses it
Help the user publish one tool from their shared computer to ToolUniverse so other scientists' AI scientists can find and run it, choosing whether only they, specific colleagues, or everyone can use it. Prepares and tests the tool for them; the user makes the final Publish click. Use when the user says "publish my tool", "put my model in Discover", "let anyone use this tool", "let only my collaborators use this tool", or "unpublish my tool".
What it loads into context
Frontmatter
107 tokensAlways available to the agent
SKILL.md
1,200 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Make sure the tool is shared first
- 2. Decide who may use it
- 3. Prepare it, then let the user publish
- 4. Check it worked
- Changing or removing it later
Files
SKILL.mdcore5,083 bytes
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# Publish one tool to ToolUniverse
Publishing puts **one** tool from a computer the user already shares into ToolUniverse, with a page in Discover if they make it public. People can run that one tool; they cannot see the computer's other tools or anything else on it.
The person you are helping is usually a scientist, not a programmer. You do the preparation; the final choice of who can use it, and the Publish click, are the user's on the website. Never ask for passwords, keys or tokens in the chat.
## 1. Make sure the tool is shared first
Publishing needs the tool to be running on a computer connected to ToolUniverse.
- If you are connected to the hosted ToolUniverse (you have `list_remote_tools`), call it and look for the user's computer ("your computer ...") and the tool.
- Otherwise ask the user whether https://connect.aiscientist.tools/remote-servers shows the computer **online** with the tool listed.
If it is not shared yet, do that first with the skill `tooluniverse-share-computer`, then come back.View the complete source on GitHubtooluniverse-rare-disease-diagnosis
When your agent uses it
Rare disease differential diagnosis from patient phenotype — HPO term matching to candidate diseases (Orphanet, OMIM), gene panel prioritization, ACMG variant interpretation, and structure-based variant analysis. Use for diagnostic odyssey assistance, phenotype-to-disease ranking, and genetic-counseling differential generation.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
2,210 tokensWhen the skill is activated
Reference documents
22,129 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Clinical Reasoning Framework (BEFORE Tools)
- Tool Parameter Corrections
- Workflow
- Evidence Grading
- Fallback Chains
- Reference Files
Scripts executed separately
scripts/clinical_patterns.py
Files
CHECKLIST.mddoc6,308 bytesDIAGNOSTIC_WORKFLOW.mddoc23,675 bytesEXAMPLES.mddoc13,697 bytesREPORT_TEMPLATE.mddoc11,129 bytesSKILL.mdcore8,899 bytesSKILL.mdcore8,899 bytesTOOLS_REFERENCE.mddoc29,133 bytesscripts/clinical_patterns.pyruns36,160 bytes
Read SKILL.md
# Rare Disease Diagnosis Advisor
Systematic diagnosis support for rare diseases using phenotype matching, gene panel prioritization, and variant interpretation across Orphanet, OMIM, HPO, ClinVar, and structure-based analysis.
**KEY PRINCIPLES**:
1. **Report-first** - Create report file FIRST, update progressively
2. **Phenotype-driven** - Convert symptoms to HPO terms before searching
3. **Multi-database triangulation** - Cross-reference Orphanet, OMIM, OpenTargets
4. **Evidence grading** - Grade diagnoses by supporting evidence strength
5. **English-first queries** - Always use English terms in tool calls
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.
View the complete source on GitHubtooluniverse-rare-disease-genomics
When your agent uses it
Rare disease genomics — disease identification (Orphanet), causative gene discovery, gene-disease validity (GenCC), variant interpretation (ClinVar), and translational research (ClinicalTrials.gov, drug repurposing for orphans). Use for rare-disease-gene curation, novel-gene-discovery analysis, and rare-disease drug-development support.
What it loads into context
Frontmatter
191 tokensAlways available to the agent
SKILL.md
4,662 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- Rare Disease Investigation Strategy
- Variant Prioritization Reasoning (CRITICAL)
- When to Use
- NOT for (use other skills instead)
- Workflow Overview
- Phase 0: Disambiguation
- Phase 1: Disease Characterization
- Phase 2: Phenotype Mapping (HPO)
- Phase 3: Causative Gene Discovery
- Phase 4: Gene-Disease Validity Assessment
- Phase 5: Pathogenic Variant Lookup
- Phase 6: Epidemiology
- Phase 6b: Metabolite-Disease Context (IEM)
- Phase 7: Clinical Trials
- Phase 8: Literature
- Evidence Grading
- Fallback Strategies
- Example Workflows
- Common Mistakes to Avoid
- Limitations
- Completeness Checklist
Files
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Rare Disease Genomics Research
## Rare Disease Investigation Strategy
The order of investigation matters: **phenotype -> disease -> gene -> variant**, not the reverse. When starting from a gene, reverse it: gene -> diseases -> expected phenotypes -> does the patient match?
Resist the urge to skip to ClinVar immediately. A "Pathogenic" ClinVar entry is only meaningful if the gene is actually causative for the disease in question with the right inheritance mode.
## Variant Prioritization Reasoning (CRITICAL)
**LOOK UP DON'T GUESS** -- when uncertain about any gene, variant, or disease association, search the database. Do not rely on memory.View the complete source on GitHubtooluniverse-regulatory-genomics
When your agent uses it
Transcription factor binding, cis-regulatory elements (cCREs), chromatin accessibility, and regulatory annotation using JASPAR (motifs), UniBind, ENCODE (cCREs, ChIP-seq), RegulomeDB (regulatory variant scoring), UCSC, and the Ensembl Regulatory Build (motif instances, evolutionarily constrained elements, TF binding matrices) — plus sequence-based deep-learning prediction of regulatory activity and non-coding variant effects (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2). Use for regulatory element annotation, TF-binding-site prediction, regulatory-region functional impact assessment, and predicting how a non-coding variant or a raw DNA sequence affects expression/chromatin/accessibility. Use this whenever a user asks what regulates a gene, whether a SNP hits a regulatory element, or to predict a non-coding variant's functional effect from sequence.
What it loads into context
Frontmatter
201 tokensAlways available to the agent
SKILL.md
7,184 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Key Tools
- Workflow
- Tool Parameter Reference
- Common Patterns
- Fallback Strategies
- Limitations
Files
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# Regulatory Genomics Research Skill
Systematic investigation of gene regulation through transcription factor binding, chromatin state, and regulatory element annotation. Integrates JASPAR (TF motifs), ENCODE (functional genomics experiments), RegulomeDB (regulatory variant scoring), and UCSC cCREs.
## Domain Reasoning
Regulatory element identification requires converging lines of evidence: sequence conservation alone is insufficient (many conserved sequences are not regulatory), chromatin accessibility is necessary but not sufficient (open chromatin can be structural), TF binding peaks require motif validation, and eQTL evidence ties the element to a transcriptional outcome. No single data type is sufficient. A high-confidence regulatory element requires at least two independent evidence types, and ideally all four.
## LOOK UP DON'T GUESS
- TF binding motifs: retrieve from `jaspar_search_matrices` and `jaspar_get_matrix`; do not describe motifs from memory.
- Experimental ChIP-seq data: search `ENCODE_search_experiments`; do not assume a TF has been profiled in a given cell type.
- cCRE annotations for a genomic region: call `UCSC_get_encode_cCREs` with exact coordinates; do not guess element types.
- Regulatory impact of a variant: query `RegulomeDB_query_variant`; never estimate regulatory importance from position alone.View the complete source on GitHubtooluniverse-regulatory-variant-analysis
When your agent uses it
Non-coding/regulatory variant interpretation — GWAS association lookup, eQTL evidence (GTEx), chromatin state (ENCODE), regulatory variant scoring (RegulomeDB, CADD), TF-binding disruption, and sequence-based deep-learning prediction (AlphaGenome/AlphaGenome Atlas) for when annotation databases are silent. Use for non-coding GWAS hit interpretation, eQTL-based gene assignment, and regulatory mechanism reasoning. Distinct from coding-variant tools.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
5,089 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Non-Coding Variant Impact Reasoning
- Workflow Overview
- Phase 0: Variant/Trait Resolution
- Phase 0.5: Sequence-Based Triage (AlphaGenome Atlas)
- Phase 1: GWAS Association Lookup
- Phase 2: eQTL Analysis
- Phase 3: Regulatory Element Annotation
- Phase 4: OpenTargets GWAS Integration
- Phase 4.5: Sequence-Based Deep-Dive (conditional)
- Phase 5: Functional Impact Synthesis
- Fallback Strategies
- Example Workflows
- Limitations
Files
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Regulatory Variant Analysis Skill
Systematic regulatory variant interpretation: discover trait associations from GWAS, map eQTL effects, annotate chromatin context, assess regulatory element overlap, predict direct sequence-level effects with AlphaGenome/AlphaGenome Atlas, and produce evidence-graded functional impact predictions for non-coding variants.
## When to Use
- "What GWAS associations exist for rs12913832?"
- "Find eQTLs for the APOE locus in brain tissue"
- "What regulatory elements overlap this variant region?"
- "Which SNPs are associated with type 2 diabetes from GWAS?"
- "Is this intronic variant in an active enhancer?"View the complete source on GitHubtooluniverse-residue-functional-mechanism-interpretation
When your agent uses it
Given a set of residues in a protein, explain WHY they are functionally critical by combining structural context (binding interface, ligand pocket, core, secondary structure), UniProt features (active sites, binding sites, PTM sites, disulfides), optional SAE feature evidence, and optional DMS data. Accepts residues from any source: DMS hotspots (top-K by max effect), ClinVar recurrent variants, literature-reported hot regions, evolutionarily conserved positions, or user-curated lists. Returns a per-cluster mechanism call: catalytic / ligand-binding / interface / structural-core / PTM / regulatory / unknown.
What it loads into context
Frontmatter
150 tokensAlways available to the agent
SKILL.md
5,889 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Required inputs (choose one entry path)
- Workflow
- Interpretation table — what the mechanism call means downstream
- Honest limitations
- Cross-references
Files
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# Residue functional mechanism interpretation
The core user question: **"Why are these residues functionally critical?"**
Answering needs more than one source: a residue in the ligand pocket means
something different from one in a catalytic triad, an interface, the
hydrophobic core, or a PTM sequon. This skill synthesizes evidence from
multiple TU tools to call a mechanism for each residue (or cluster of
adjacent residues).
The residues can come from **any source** — this skill is agnostic to where
they came from:
| Residue source | Typical call pattern |View the complete source on GitHubtooluniverse-rnaseq-deseq2
When your agent uses it
RNA-seq differential expression analysis with DESeq2, edgeR, and limma-voom — DEG lists, fold changes, dispersion estimation, design formulas including covariates, multi-condition contrasts, and Venn-set operations across groups. Routes across DESeq2 (default), edgeR (QL-F / exact test for small replicate counts), and limma-voom (large n / complex designs). Use when you have a count matrix + metadata, want to find DEGs, or need dispersion/PCA/clustering analysis. Includes RULE ZERO precedence (read executed.ipynb if present).
What it loads into context
Frontmatter
143 tokensAlways available to the agent
SKILL.md
9,347 tokensWhen the skill is activated
Reference documents
23,590 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- PRIMARY SCRIPTS — use these FIRST before writing custom code
- CRITICAL — Read before writing any code
- Workspace isolation (CRITICAL)
- Domain Reasoning
- LOOK UP DON'T GUESS
- Core Principles
- When to Use
- Required Packages
- Analysis Workflow
- Common Patterns
- Error Quick Reference
- Interpretation Framework
- Known Limitations
- Reference Files
- Utility Scripts
- Analysis conventions
- Density / per-chromosome calculation conventions
Scripts executed separately
scripts/convert_rds_to_csv.pyscripts/format_deseq2_output.pyscripts/gene_length_correlation.pyscripts/load_count_matrix.pyscripts/multi_strain_venn.pyscripts/one_way_anova_f.pyscripts/pca_variance.pyscripts/r_deseq2_wrapper.pyscripts/r_edger_limma_wrapper.py
Files
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Read SKILL.md
# RNA-seq Differential Expression Analysis (DESeq2)
## PRIMARY SCRIPTS — use these FIRST before writing custom code
The four scripts below are deterministic, audited wrappers that handle the
ambiguity in DESeq2 / correlation / PCA / ANOVA questions by emitting
EVERY common interpretation in one call. Reading their output and
matching the variant the published notebook used is more reliable than
re-deriving the answer from scratch.
All four scripts honor workspace isolation: they ONLY write to `--workdir`
(or `/tmp/...` by default). They never touch the input data folder. Always
pass `--workdir /tmp/<run-name>` when you need intermediate files.
View the complete source on GitHubtooluniverse-scientific-peer-review
When your agent uses it
Review scientific manuscripts, abstracts, methods, statistical analyses, results, discussions, protocols, and experimental designs using calibrated rubrics and prioritized revision advice. Use for journal-style peer review, study-design scoring, methodology or statistics critique, novelty and literature-context assessment, interpretation checks, scientific writing review, reproducibility review, ethics screening, protocol optimization, or a combined manuscript review. This is the host-agent replacement for the corresponding ToolUniverse AgenticTool reviewers and does not require a ToolUniverse backend LLM API.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
1,248 tokensWhen the skill is activated
Reference documents
2,819 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow
- Trust Boundary
- Scope and Evidence Boundary
- Review Method
- Long or Multi-Part Manuscripts
- Output
- Quality Checks
Files
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# Scientific Peer Review
Perform the review in the host agent. Do not call the legacy AgenticTool reviewers unless the user explicitly requests legacy/backend execution.
## Workflow
1. Identify the material supplied and the review dimensions requested.
2. Identify the study design. Read [references/study-design-guides.md](references/study-design-guides.md) when design-specific checks apply.
3. Read [references/review-rubrics.md](references/review-rubrics.md) and apply only the relevant rubrics.
4. Separate text-grounded assessment from claims requiring external verification.
5. Assess each requested criterion, cite concrete evidence from the supplied material, and propose actionable revisions.
6. Synthesize the highest-impact issues instead of returning disconnected rubric scores.
## Trust BoundaryView the complete source on GitHubtooluniverse-scientific-problem-selection
When your agent uses it
Conversational framework for choosing what scientific problem to work on, pitching a new project idea, troubleshooting a stuck project, or making a strategic research decision. Use when users say "I have an idea for a project", "I'm stuck on my research", "what should I work on", "help me evaluate this project's risk", "is this idea worth pursuing", "help me decide between two research directions", "I need strategic advice about my research", or ask to pitch/refine/de-risk a hypothesis before running any experiments or analyses. Grounds the conversation in real literature-precedent and funding-landscape lookups rather than pure opinion.
What it loads into context
Frontmatter
148 tokensAlways available to the agent
SKILL.md
2,118 tokensWhen the skill is activated
Reference documents
3,519 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Three Entry Points
- Core Principles
- Workflow by Entry Point
- Precedent and Fundability Grounding
- Output
- Reference Files
- Attribution
Files
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# Scientific Problem Selection
A structured conversation for the decision that happens *before* any experiment, dataset pull, or analysis: which problem is actually worth the next few months (or years) of work. Problem choice dominates outcome more than execution quality — a well-executed answer to a low-value question is still a low-value result.
**LOOK UP, DON'T GUESS**: Every claim about novelty, precedent, or fundability in this skill must be checked against real tools, not asserted from memory. See `references/precedent-checking.md` for the exact tool names and when to call each one.
---
## When to Use This Skill
Apply when users:
- Pitch a new project idea and want it stress-tested before committing time to it
- Say they are stuck, blocked, or a project stopped working and they need to decide what to do next
- Ask a strategic question about which of several directions to pursueView the complete source on GitHubtooluniverse-scvi-tools
When your agent uses it
Deep learning for single-cell omics using scvi-tools — probabilistic (VAE-based) batch correction and integration with scVI, semi-supervised integration and label transfer with scANVI, CITE-seq RNA+protein joint modeling with totalVI, scATAC-seq analysis with PeakVI, multiome RNA+ATAC joint modeling with MultiVI, spatial transcriptomics cell-type deconvolution with DestVI, and RNA velocity with veloVI. Use when scVI, scANVI, totalVI, PeakVI, MultiVI, DestVI, or veloVI are mentioned by name, or when a request needs deep-learning-based batch correction, reference mapping / scArches-style query-to-reference mapping, denoising single-cell counts, joint modeling of two modalities (RNA+protein, RNA+ATAC, single-cell+spatial), or a variational-autoencoder latent space for single-cell data — as opposed to classic scanpy/Harmony-based integration.
What it loads into context
Frontmatter
215 tokensAlways available to the agent
SKILL.md
1,342 tokensWhen the skill is activated
Reference documents
3,454 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to Use This Skill
- Model Selection
- Workflow
- Reference Documentation
Scripts executed separately
scripts/_scvi_common.pyscripts/differential_expression.pyscripts/prepare_data.pyscripts/train_model.pyscripts/transfer_labels.pyscripts/validate_adata.py
Files
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# scvi-tools Deep Learning for Single-Cell Omics
Local, guided workflows for training and applying scvi-tools' probabilistic
deep-learning models on a user's own AnnData files.
## Honesty contract (read first)
This skill trains real neural networks via the real `scvi-tools` package. It
must never fabricate a latent space, a training curve, or a DE result.
1. **Preflight before anything.** Every script checks `import scvi` (and
`anndata`/`scanpy`/`numpy`) before touching data. If scvi-tools is not
installed, the script prints the exact `pip install scvi-tools` command
and exits 0 — it does not estimate, guess, or describe hypotheticalView the complete source on GitHubtooluniverse-sdk
When your agent uses it
Build AI scientist systems with the ToolUniverse Python SDK for scientific research. Covers the 3 calling patterns (`tu.run` portable dict API, `tu.tools.X` function API, direct class instantiation), tool loading, batch execution, MCP server integration, and embedding-based tool search. Use for SDK programming, custom tool composition, benchmarking pipelines, and integrating ToolUniverse into research workflows.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
1,165 tokensWhen the skill is activated
Reference documents
3,125 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Installation
- Quick Start
- Core Patterns
- Configuration
- Critical Notes
- Error Handling
- Tool Categories
- Resources
Files
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# ToolUniverse Python SDK
**3 calling patterns -- start with pattern 1:**
1. `tu.run({"name": ..., "arguments": ...})` -- single tool call, dict API (most portable)
2. `tu.tools.ToolName(param=value)` -- function API (recommended for interactive use)
3. Direct class instantiation -- advanced, bypasses caching/hooks
## Installation
```bash
pip install tooluniverse # Standard
pip install tooluniverse[embedding] # Embedding search (GPU)
pip install tooluniverse[all] # All features
```View the complete source on GitHubtooluniverse-self-review
When your agent uses it
Review existing work against the user's actual goal and surface evidence-backed strengths, gaps, risks, and next fixes. Use when asked to eval, evaluate, review, assess, or check current/this/my/our work; decide whether a task is complete; build a definition-of-done checklist or rubric; or perform grading, LLM-as-judge, Qworld, or RET evaluation. Treat plain eval/review requests as qualitative: resolve "current work" from the conversation, artifacts, files, or diff, and never assign numeric scores unless the user explicitly requests scores, grades, points, ratings, weighted criteria, Qworld, or RET. Do not use for implementing automated eval suites, tests, graders, or benchmarks.
What it loads into context
Frontmatter
179 tokensAlways available to the agent
SKILL.md
1,942 tokensWhen the skill is activated
Reference documents
967 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Non-Negotiable Rules
- Resolve the Review Target
- Choose One Mode
- Qualitative Review Workflow (Default)
- Checklist and Unscored Rubric Modes
- Scored Evaluation Mode (Explicit Opt-In Only)
- Examples of Correct Routing
- Evidence and Honesty
Files
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# Self-Review: Understand the Target, Then Review
Review the actual work the user means against the goal it was meant to satisfy. Default
to a concise qualitative assessment. Scoring and the full Qworld Recursive Expansion
Tree (RET) are opt-in.
## Non-Negotiable Rules
1. **Do not confuse the evaluation request with the evaluated task.** In requests such as
"eval current work", that sentence is an instruction to review. The task being judged
is the preceding user goal; the work is the current result, implementation, draft,
plan, or progress.
2. **Do not equate `eval` with scoring.** `Eval`, `evaluate`, `review`, `assess`, and
`check` mean qualitative review unless the user explicitly asks for a score, grade,View the complete source on GitHubtooluniverse-sequence-analysis
When your agent uses it
Biological sequence analysis — gene/protein sequence retrieval (NCBI, Ensembl, UniProt), nucleotide/protein search, ortholog discovery, and FASTQ QC + alignment workflows (Trimmomatic, BWA, samtools, coverage depth). Use for sequence retrieval, sequence comparison, FASTQ QC analysis, and read alignment pre-processing.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
6,806 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- ⚠<U+FE0F> TOP-OF-MIND RULE: Trimmomatic "reads completely discarded" = `F + R + 2*D`, summed across samples
- RULE ZERO — Check for pre-computed results FIRST
- FASTQ QC, Trimmomatic, and read alignment (when raw reads are present)
- When to Use
- Workflow
- Phase 1: Gene Identification and Summary
- Phase 2: Nucleotide Sequence Search and Retrieval
- Phase 3: Protein Sequence Retrieval
- Phase 4: Ortholog and Comparative Analysis
- Phase 5: Domain Architecture and Homology
- Phase 6: Variant and Clinical Context
- Tool Parameter Quick Reference
- Fallbacks
- Sequence Analysis Reasoning (CRITICAL)
- Reasoning for Protein Feature Questions
- Bundled Computation Scripts
- Interpretation Framework
- Answer Formatting (CRITICAL)
- Peptide & Foldamer Structure
- Limitations
Scripts executed separately
scripts/amino_acids.pyscripts/biology_facts.pyscripts/sequence_tools.pyscripts/translate_dna.py../../../skills/tooluniverse-sequence-analysis/scripts/amino_acids.py../../../skills/tooluniverse-sequence-analysis/scripts/biology_facts.py../../../skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py../../../skills/tooluniverse-sequence-analysis/scripts/translate_dna.py
Files
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# Biological Sequence Analysis
## ⚠<U+FE0F> TOP-OF-MIND RULE: Trimmomatic "reads completely discarded" = `F + R + 2*D`, summed across samples
When a question asks about Trimmomatic "reads completely discarded" / "reads thrown out" /
"reads not in any output", do NOT report the `Dropped` field alone. `Dropped` counts
PAIRS where both mates failed; each pair = 2 individual reads. Plus the Forward-only and
Reverse-only buckets also discard one read per pair.
```
reads_discarded = sum over samples of (Forward_Only + Reverse_Only + 2 * Dropped)
```
❌ WRONG: `sum(Dropped per sample)` — typically reports ~thousands, GT is 100×+ higherView the complete source on GitHubtooluniverse-sequence-retrieval
When your agent uses it
Retrieve DNA/RNA/protein sequences from NCBI and ENA with disambiguation. Quality hierarchy: RefSeq (NM_/NP_) > RefSeq predicted (XM_/XP_) > GenBank submissions. Use for fetching specific sequences by accession, gene-symbol-to-sequence lookup, transcript-isoform retrieval, and curated-vs-raw-submission preference.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
4,696 tokensWhen the skill is activated
Reference documents
1,226 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- Workflow
- Phase 0: Clarification (When Needed)
- Phase 1: Gene/Organism Disambiguation
- Phase 2: Data Retrieval (Internal)
- Phase 3: Report Sequence Profile
- Reasoning Framework
- Error Handling
- Tool Reference
- Search Parameters Reference
- Whole-Organism Reference Proteomes (UniProt)
- Sequence Archive and Clustering (UniParc, UniRef)
- Subcellular Location Vocabulary (UniProt Locations)
- Sample Metadata (EBI BioSamples)
- Organism Taxonomy (EBI Taxonomy)
- Cross-Domain Quick Lookup (BioThings Gateway)
- Cross-Database ID Resolution (Bioregistry, Identifiers.org, TogoID)
- EBI dbfetch (One Interface, Many Databases)
Files
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# Biological Sequence Retrieval
Retrieve DNA, RNA, and protein sequences with proper disambiguation and cross-database handling.
**IMPORTANT**: Always use English terms in tool calls. Only try original-language terms as fallback. Respond in the user's language.
**LOOK UP DON'T GUESS**: Never assume accession numbers or sequence versions. Always retrieve and verify from NCBI or ENA.
## Domain Reasoning
Sequence quality hierarchy: RefSeq (NM_/NP_ = curated) > RefSeq predicted (XM_/XP_) > GenBank (submitted). Prefer the MANE Select transcript for human canonical isoforms. Check version numbers -- annotations improve across versions.
## Workflow
View the complete source on GitHubtooluniverse-share-computer
When your agent uses it
Share tools that live on this computer (the user's own Python function, script or notebook code, a model, a program they already run, or ToolUniverse's reviewed GPU models) so the user's and their colleagues' AI scientists can use them through ToolUniverse. You do the work; the user only answers questions and clicks Allow once in a browser. Use when the user says things like "share my model with my lab", "let my colleague use this script", "make this function a tool", "put my GPU models on ToolUniverse", or "keep my tool running".
What it loads into context
Frontmatter
127 tokensAlways available to the agent
SKILL.md
3,152 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 0. Check you can do this here
- 1. Find out what to share (ask, don't assume)
- 2. Install ToolUniverse sharing (once per computer)
- 3. Prepare what to share
- 4. Share it (the user clicks Allow once)
- 5. Check that it really works
- 6. Keep it running (ask first)
- 7. Let colleagues use it
- Stopping and undoing
- When something goes wrong
Files
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# Share a computer's tools through ToolUniverse
The person you are helping is usually a scientist, not a programmer. Do every technical step yourself. Ask them only what you cannot find out, in plain words, one question at a time. Never ask them to edit code, read logs, or type commands; never paste or ask for keys, tokens or passwords in the chat.
Nothing leaves their computer except what the chosen tool returns. ToolUniverse only relays calls to it; their files, data and passwords stay where they are, and only the tools they pick are reachable.
## 0. Check you can do this here
You must be able to run commands **on the computer that has the code or model** (Claude Code, Codex, or another agent with a terminal there). If you cannot (for example you are Claude in a browser), say so plainly and stop:
> Sharing has to be set up on the computer that has your model. Open Claude Code or Codex on that computer and ask it the same thing; it can use this guide too.
**Sandboxes.** Several steps need the internet and write to the user's home folder (installing, signing in, sharing). If your environment runs commands in a sandbox (Codex does by default), a step can fail with errors such as `Could not resolve host` or `Permission denied`. That is your own safety setting, not a broken installer: ask the user to approve running the step with network access, in one plain sentence ("I need internet access to install ToolUniverse's sharing tool. Allow it?"). Combine steps into as few commands as you can, so the user approves as few times as possible.
View the complete source on GitHubtooluniverse-single-cell
When your agent uses it
Single-cell RNA-seq analysis with scanpy/anndata — h5ad data loading, scRNA-seq quality control and QC gating (n_genes_by_counts, total_counts, mitochondrial percent / pct_counts_mt, pct_counts_ribo, doublet detection with Scrublet/scDblFinder, ambient RNA / SoupX awareness, empty-droplet filtering, MAD-based thresholds), normalization, dimensionality reduction (PCA, UMAP, t-SNE), clustering (Leiden, Louvain), marker gene identification, cell-type annotation, pseudotime/trajectory analysis. Use for any scRNA-seq workflow, including deciding which cells to filter, flag, or investigate before downstream analysis.
What it loads into context
Frontmatter
157 tokensAlways available to the agent
SKILL.md
6,008 tokensWhen the skill is activated
Reference documents
21,328 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- RULE ZERO — Check for pre-computed results FIRST
- LOOK UP, DON'T GUESS
- When to Use This Skill
- Core Principles
- Required Packages
- Workflow Decision Tree
- Data Loading
- Quality Control and QC Gating (do this BEFORE downstream analysis)
- Complete Pipeline (Quick Reference)
- Differential Expression Decision Tree
- Statistical Tests (Quick Reference)
- Batch Correction (Harmony)
- ToolUniverse Integration
- Scanpy vs Seurat Equivalents
- Reasoning Framework for Result Interpretation
- Troubleshooting
- Reference Documentation
- Analysis Conventions
Scripts executed separately
scripts/find_markers.pyscripts/normalize_data.pyscripts/qc_metrics.pyscripts/scrna_qc.py
Files
.env.templateasset499 bytesSKILL.mdcore23,220 bytesSKILL.mdcore23,220 bytesSKILL.mdcore23,220 bytesSKILL.mdcore23,220 bytesSKILL.mdcore23,220 bytesSKILL.mdcore23,220 bytesSKILL.mdcore23,220 bytesSKILL.mdcore23,220 bytesSKILL.mdcore23,220 bytesanalysis_patterns.mddoc6,416 bytesreferences/cell_communication.mddoc20,360 bytesreferences/clustering_guide.mddoc9,616 bytesreferences/marker_identification.mddoc5,540 bytesreferences/scanpy_workflow.mddoc10,327 bytesreferences/scrna_qc.mddoc10,722 bytesreferences/seurat_workflow.mddoc4,510 bytesreferences/trajectory_analysis.mddoc2,842 bytesreferences/troubleshooting.mddoc8,701 bytesscripts/find_markers.pyruns3,626 bytesscripts/normalize_data.pyruns2,608 bytesscripts/qc_metrics.pyruns4,296 bytesscripts/scrna_qc.pyruns6,904 bytes
Read SKILL.md
# Single-Cell Genomics and Expression Matrix Analysis
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
---
Comprehensive single-cell RNA-seq analysis and expression matrix processing using scanpy, anndata, scipy, and ToolUniverse.View the complete source on GitHubtooluniverse-small-molecule-discovery
When your agent uses it
Small molecule identification, characterization, and procurement — PubChem, ChEMBL, BindingDB, ADMET-AI, SwissADME, eMolecules, Enamine. Covers compound name to structure to activity to ADMET properties to commercial sourcing. Use for chemical biology, lead identification, probe selection, and the full small-molecule discovery pipeline.
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
4,439 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Key Tools
- Workflow
- Tool Parameter Reference
- Common Patterns
- Fallback Chains
- Limitations
Files
SKILL.mdcore15,970 bytes
Read SKILL.md
# Small Molecule Discovery Skill
Systematic small molecule identification, characterization, and sourcing using PubChem, ChEMBL, BindingDB, ADMET-AI, SwissADME, eMolecules, and Enamine. Covers the full pipeline from compound name to structure, activity, ADMET properties, and commercial procurement.
## Domain Reasoning
Drug-likeness is not a binary property. Lipinski's Rule of 5 was derived from orally administered, passively absorbed drugs and has many well-known exceptions: natural products, macrocycles, PROTACs, and many approved drugs violate one or more rules. The relevant question is not "does this pass Ro5?" but "does this compound's physicochemical profile match the requirements of the target, the intended route of administration, and the therapeutic context?" Focus on the specific requirements, not rigid rules.
## LOOK UP DON'T GUESS
- Compound identity (CID, ChEMBL ID, SMILES): call `PubChem_get_CID_by_compound_name` and `ChEMBL_search_molecules`; do not assume IDs from memory.
- ADMET properties: run `SwissADME_calculate_adme` or `ADMETAI_predict_*` on the actual SMILES; do not estimate logP, TPSA, or bioavailability.
- Binding affinities against a target: query `ChEMBL_search_activities` or `BindingDB_get_ligands_by_uniprot`; never cite IC50 values from memory.
- Commercial availability: check `eMolecules_search` or `Enamine_search_catalog`; do not assume availability.View the complete source on GitHubtooluniverse-spatial-omics-analysis
When your agent uses it
Spatial multi-omics interpretation pipeline. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into biological insights via domain-by-domain characterization, cell-type composition, spatial gene expression patterns, RNA+protein+metabolite integration. Use for Visium, MERFISH, seqFISH, Slide-seq, spatial proteomics, and spatial multi-omics interpretation. Goes beyond statistics to disease mechanisms and therapeutic opportunities.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
3,076 tokensWhen the skill is activated
Reference documents
7,651 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Input Parameters
- Spatial Omics Integration Score (0-100)
- Analysis Phases Overview
- Report Structure
- Completeness Checklist
- Common Use Cases
- Reference Files
- Summary
Files
SKILL.mdcore12,644 bytesSKILL.mdcore12,644 bytesSKILL.mdcore12,644 bytesphase-procedures.mddoc10,709 bytesreference-data.mddoc4,371 bytesreport-template.mddoc8,217 bytestool-reference.mddoc5,769 bytes
Read SKILL.md
# Spatial Multi-Omics Analysis Pipeline
Comprehensive biological interpretation of spatial omics data. Transforms spatially variable genes (SVGs), domain annotations, and tissue context into actionable biological insights.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Domain-by-domain analysis** - Characterize each spatial region independently before comparison
3. **Gene-list-centric** - Analyze user-provided SVGs and marker genes with ToolUniverse databases
4. **Biological interpretation** - Go beyond statistics to explain biological meaning of spatial patterns
5. **Disease focus** - Emphasize disease mechanisms and therapeutic opportunities when disease context is provided
6. **Evidence grading** - Grade all evidence as T1 (human/clinical) to T4 (computational)
7. **Multi-modal thinking** - Integrate RNA, protein, and metabolite information when available
8. **Validation guidance** - Suggest experimental validation approaches for key findings
9. **Source references** - Every statement must cite tool/database sourceView the complete source on GitHubtooluniverse-spatial-transcriptomics
When your agent uses it
Spatial transcriptomics analysis — Visium, MERFISH, seqFISH, Slide-seq. Maps gene expression to tissue architecture, identifies spatially variable genes (SVGs), tissue-domain segmentation, and cell-cell interaction inference. Use for spatial gene-expression questions, tissue architecture analysis, and SVG identification.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
4,600 tokensWhen the skill is activated
Reference documents
3,117 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- When to Use This Skill
- Core Capabilities
- Supported Platforms
- Workflow Overview
- Phase Summaries
- Integration with ToolUniverse Skills
- ToolUniverse Data Retrieval Tools
- Example Use Cases
- Quantified Minimums
- Reasoning Framework
- Programmatic Access (Beyond Tools)
- Limitations
- References
- Reference Files
Files
SKILL.mdcore19,258 bytesSKILL.mdcore19,258 bytesSKILL.mdcore19,258 bytesSKILL.mdcore19,258 bytesSKILL.mdcore19,258 bytescode_examples.mddoc7,601 bytesreport_template.mddoc4,003 bytes
Read SKILL.md
# Spatial Transcriptomics Analysis
Comprehensive analysis of spatially-resolved transcriptomics data to understand gene expression patterns in tissue architecture context. Combines expression profiling with spatial coordinates to reveal tissue organization, cell-cell interactions, and spatially variable genes.
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
## When to Use This Skill
**Triggers**:
- User has spatial transcriptomics data (Visium, MERFISH, seqFISH, etc.)
- User has *only* an H&E histology image and no spatial assay at all -- `DeepSpotM_predict_gene_expression` (local model, no API/key) predicts spatial gene expression for a 224x224 H&E tile directly, a virtual readout when no real spatial data exists yet. Treat its output as a model prediction to guide follow-up (e.g. which region to actually assay), not a substitute for the platforms above when real spatial data is the goal.
- Questions about tissue architecture or spatial organization
- Spatial gene expression pattern analysisView the complete source on GitHubtooluniverse-statistical-modeling
When your agent uses it
Statistical modeling — linear/logistic/ordinal/Poisson regression, ANOVA, Kruskal-Wallis, chi-square, Mann-Whitney, Cox survival, spline fits (R `ns()`), odds ratios, Cohen's d, F-statistic, p-value computation. Specializes in clinical-trial AE analysis (SDTM DM/AE), severity ordinal regression, and per-feature stat workflows.
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
9,677 tokensWhen the skill is activated
Reference documents
28,141 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- RULE ZERO — Check for pre-computed results FIRST
- PRIMARY SCRIPTS — use these FIRST
- Workspace isolation (CRITICAL)
- CRITICAL — Read before writing any code
- COMPUTE, DON'T DESCRIBE
- LOOK UP, DON'T GUESS
- Features
- When to Use
- Model Selection Decision Tree
- Workflow
- Common Patterns
- Statsmodels vs Scikit-learn
- Python Package Requirements
- Key Principles
- Reasoning Framework for Result Interpretation
- Completeness Checklist
- Bundled Scripts
- File Structure
- ToolUniverse Integration
- References
- Analysis conventions
- Support
Scripts executed separately
scripts/expression_anova.pyscripts/format_statistical_output.pyscripts/logistic_regression_or.pyscripts/model_diagnostics.pyscripts/power_analysis.pyscripts/prepare_ae_cohort.pyscripts/r_natural_spline_regression.pyscripts/sdtm_ordinal_logistic.pyscripts/spline_model_compare.pyscripts/stat_tests.py../../../skills/tooluniverse-statistical-modeling/scripts/expression_anova.py../../../skills/tooluniverse-statistical-modeling/scripts/prepare_ae_cohort.py
Files
EXAMPLES.mddoc9,238 bytesQUICK_START.mddoc5,752 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesSKILL.mdcore37,592 bytesTOOLS_REFERENCE.mddoc5,996 bytesanova_and_tests.mddoc4,397 bytesreferences/common_patterns.mddoc14,180 bytesreferences/cox_regression.mddoc14,247 bytesreferences/linear_models.mddoc11,032 bytesreferences/logistic_regression.mddoc9,871 bytesreferences/ordinal_logistic.mddoc12,949 bytesreferences/troubleshooting.mddoc12,539 bytesscripts/expression_anova.pyruns5,802 bytesscripts/format_statistical_output.pyruns11,039 bytesscripts/logistic_regression_or.pyruns11,628 bytesscripts/model_diagnostics.pyruns15,090 bytesscripts/power_analysis.pyruns5,978 bytesscripts/prepare_ae_cohort.pyruns6,394 bytesscripts/r_natural_spline_regression.pyruns10,868 bytesscripts/sdtm_ordinal_logistic.pyruns9,260 bytesscripts/spline_model_compare.pyruns11,517 bytesscripts/stat_tests.pyruns22,652 bytes../../../skills/tooluniverse-statistical-modeling/scripts/expression_anova.pyruns5,802 bytes../../../skills/tooluniverse-statistical-modeling/scripts/prepare_ae_cohort.pyruns6,394 bytes
Read SKILL.md
# Statistical Modeling for Biomedical Data Analysis
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
---
## PRIMARY SCRIPTS — use these FIRSTView the complete source on GitHubtooluniverse-stem-cell-organoid
When your agent uses it
Stem cell, iPSC, and organoid research — pluripotency markers, differentiation protocol pathways, lineage commitment factors, organoid model selection. Use for iPSC characterization, differentiation protocol design via developmental-pathway recapitulation, and organoid-model selection for disease modeling.
What it loads into context
Frontmatter
75 tokensAlways available to the agent
SKILL.md
2,049 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- Core Tools
- Workflow
- Evidence Grading
- Limitations
Files
SKILL.mdcore8,570 bytesSKILL.mdcore8,570 bytes
Read SKILL.md
# Stem Cell & Organoid Research
Pipeline for investigating stem cell biology, iPSC characterization, organoid models, and cell differentiation using ToolUniverse tools.
## Reasoning Strategy
Stem cell differentiation follows developmental biology — to make any target cell type from iPSCs, the protocol must mimic the embryonic signaling pathway that generates that cell type in vivo. For neural induction: inhibit BMP and TGF-beta (dual SMAD inhibition). For cardiomyocytes: activate WNT then inhibit WNT. For pancreatic beta cells: activate Activin/Nodal → FGF → Notch inhibition → BMP in sequence. The order and timing of growth factors matters critically — adding BMP4 during neural induction will redirect cells toward mesoderm. Mouse and human stem cells differ in their signaling requirements (LIF/STAT3 for mouse naive pluripotency; FGF/ERK for human primed pluripotency), so protocols are not interchangeable. Organoids recapitulate some but not all organ features — always assess maturation state (fetal vs. adult gene expression) before drawing disease-relevance conclusions.
**LOOK UP DON'T GUESS**: Do not assume which markers define a target cell type or which signaling pathway drives differentiation — query `CellMarker_search_by_cell_type` for markers and `kegg_search_pathway` for the relevant pathway. Do not assume organoid fidelity; look up published CellxGene or HCA atlas data for comparison.
**Key principles**:
1. **Marker-based identity** — stem cell identity is defined by marker expression profiles (OCT4, SOX2, NANOG for pluripotency)
2. **Differentiation is a trajectory** — not a binary state; analyze intermediate progenitor stages
3. **Organoid ≠ organ** — organoids recapitulate some but not all organ features; always note limitationsView the complete source on GitHubtooluniverse-structural-proteomics
When your agent uses it
Structural biology plus proteomics integration for drug target validation. Combines PDB experimental structures, AlphaFold predictions, GPCRdb, SAbDab antibody structures, ProteinsPlus binding-site prediction, BindingDB ligand-affinity data, BMRB NMR data, CATH/InterPro fold classification, intrinsic-disorder prediction (MobiDB/DisProt/IUPred3), membrane-protein topology (OPM/TopDB/PDBTM/ChannelsDB), enzyme catalytic-site data (M-CSA), PDBe ligand/compound chemistry, and MD simulation trajectory metadata (MDDB). Use for druggability assessment, binding-site characterization, ligand-pocket analysis, structural-confidence scoring (resolution, pLDDT), antibody-target interface analysis, disordered-region assessment, membrane-protein topology, catalytic-mechanism lookup, and finding published MD simulations for a target.
What it loads into context
Frontmatter
197 tokensAlways available to the agent
SKILL.md
7,499 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Domain Reasoning
- Tool Inventory
- Workflow 1: Find All Structures for a Drug Target
- Workflow 4: NMR Structural/Dynamics Data (BMRB)
- Workflow 5: Fold and Domain-Signature Classification (CATH + InterPro Member Databases)
- Workflow 5b: Whole-Protein Domain Architecture and Pfam Clans
- Workflow 6: Intrinsic Disorder Assessment (MobiDB + DisProt + IUPred3)
- Workflow 7: Membrane Protein Structure (OPM + TopDB + PDBTM + ChannelsDB)
- Workflow 8: Enzyme Catalytic Mechanism (M-CSA)
- Workflow 2: Identify Binding Pocket Ligands
- Workflow 9: MD Simulation Trajectory Metadata (MDDB)
- Workflow 3: Cross-Validate Drug Binding
- Tool Parameter Gotchas
- Evidence Grading
- Interpretation
- Limitations
Files
SKILL.mdcore28,483 bytes
Read SKILL.md
# Structural Proteomics for Drug Target Validation
Comprehensive structural data integration using ToolUniverse tools across PDB, AlphaFold, GPCRdb, SAbDab, and proteomics databases for drug target validation.
## LOOK UP DON'T GUESS
- PDB structures/resolutions: `PDBeSIFTS_get_best_structures` and `RCSBGraphQL_get_structure_summary`
- AlphaFold confidence: `alphafold_get_summary`
- Ligands/affinities: `PDBe_get_structure_ligands` and `BindingDB_get_ligands_by_uniprot`
- Protein-protein binding mutations (ddG): `SKEMPI_search_by_structure`/`SKEMPI_search_by_protein`/`SKEMPI_get_mutation` -- BindingDB's protein-protein equivalent, for interface-mutation affinity effects rather than small-molecule ligands
- Druggability: `ProteinsPlus_predict_binding_sites`
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.View the complete source on GitHubtooluniverse-structural-variant-analysis
When your agent uses it
Structural variant (SV) clinical interpretation: deletions, duplications, inversions, translocations, complex rearrangements. Applies ACMG-adapted criteria with ClinGen HI/TS dosage scores, gnomAD frequencies, and ClinVar evidence. Produces 5-tier classification with explicit per-criterion evidence. Use for clinical genomics SV review, dosage-sensitivity assessment, breakpoint analysis, and CNV pathogenicity calls. Gene-dosage-driven reasoning.
What it loads into context
Frontmatter
115 tokensAlways available to the agent
SKILL.md
2,715 tokensWhen the skill is activated
Reference documents
18,137 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- Triggers
- SV Pathogenicity Reasoning (Start Here)
- Workflow Overview
- Phase 1: SV Identity & Classification
- Phase 2: Gene Content Analysis
- Phase 3: Dosage Sensitivity Assessment
- Phase 4: Population Frequency Context
- Phase 5: Pathogenicity Scoring
- Phase 6: Literature & Clinical Evidence
- Phase 7: ACMG-Adapted Classification
- Output
- Required Tools Reference
- When NOT to Use This Skill
- Reference Files
- External References
Files
ANALYSIS_PROCEDURES.mddoc11,341 bytesCLASSIFICATION_GUIDE.mddoc8,818 bytesEXAMPLES.mddoc45,028 bytesREPORT_TEMPLATE.mddoc4,912 bytesSKILL.mdcore11,543 bytes
Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Structural Variant Analysis Workflow
Systematic analysis of structural variants (deletions, duplications, inversions, translocations, complex rearrangements) for clinical genomics interpretation using ACMG-adapted criteria.
**LOOK UP DON'T GUESS** - Always retrieve ClinGen HI/TS scores, gnomAD frequencies, and ClinVar evidence from tools. Do not infer dosage sensitivity from gene function alone.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create SV_analysis_report.md FIRST, then populate progressively
2. **ACMG-style classification** - Pathogenic/Likely Pathogenic/VUS/Likely Benign/Benign with explicit evidence
3. **Evidence grading** - Grade all findings by confidence level (High/Moderate/Limited)
4. **Dosage sensitivity critical** - Gene dosage effects drive SV pathogenicityView the complete source on GitHubtooluniverse-synthetic-biology-parts
When your agent uses it
Look up synthetic biology genetic parts (promoters, RBS, CDS/genes, terminators, reporters, composite devices) from the SynBioHub repository, including the iGEM Registry of Standard Biological Parts. Use when someone asks to "find a genetic part", "look up a BioBrick", "search SynBioHub", "get the sequence for BBa_...", "browse iGEM part collections", "find a promoter/RBS/terminator for a genetic circuit", or "what parts are available for [organism/function]". NOT for plasmid backbone/vector lookup by catalog ID (use tooluniverse-molecular-cloning's Addgene coverage), NOT for primer design once a part sequence is chosen (use tooluniverse-primer-design). Honest: SynBioHub disabled anonymous public REST access upstream — every unauthenticated call currently returns 401/HTML-login-page, verified live. This skill explains that limitation plainly and how to unlock real access (SYNBIOHUB_API_TOKEN) rather than pretending the tools work with no setup.
What it loads into context
Frontmatter
235 tokensAlways available to the agent
SKILL.md
1,876 tokensWhen the skill is activated
Reference documents
1,629 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first) — verify auth status before promising results
- When to Use This Skill
- Workflow
- Downstream Handoff
- Reference
Files
SKILL.mdcore7,519 bytesSKILL.mdcore7,519 bytesreferences/synbiohub_tool_reference.mddoc6,104 bytes
Read SKILL.md
# Synthetic Biology Parts Lookup (SynBioHub)
Search and retrieve genetic parts — promoters, ribosome binding sites (RBS),
coding sequences, terminators, reporters, and composite genetic devices —
from SynBioHub, the standard synthetic-biology parts repository that hosts
the iGEM Registry of Standard Biological Parts (20,000+ BioBricks), CIDAR
Lab parts, Free Genes libraries, and other public SBOL collections.
## Honesty contract (read first) — verify auth status before promising results
**Live-verified as of this writing: every anonymous call to all 3 tools below
fails.** SynBioHub disabled anonymous access to its public REST endpoints
upstream. Confirmed by actually running each tool with no token set:
View the complete source on GitHubtooluniverse-systems-biology
When your agent uses it
Systems biology and pathway analysis integrating Reactome, KEGG, WikiPathways, BioCarta, NCI-Nature Pathway Interaction Database. Multi-database pathway enrichment, protein-pathway relationships, network reasoning. Use for pathway analysis on a gene list, multi-source pathway concordance, and systems-level interpretation across databases.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
6,883 tokensWhen the skill is activated
Reference documents
3,705 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- COMPUTE, DON'T DESCRIBE
- Domain Reasoning: Enrichment vs Causation
- Core Databases Integrated
- Workflow Overview
- Phase 1: Pathway Enrichment Analysis
- Phase 2: Protein-Pathway Mapping
- Phase 3: Keyword-Based Pathway Search
- Phase 4: Top-Level Pathway Catalog
- Output Structure
- Tool Parameter Reference
- Domain Reasoning: Enzyme Kinetics & Metabolic Analysis
- Fallback Strategies
- Limitations & Known Issues
- GO Term Lookup, KEGG BRITE Classification, and Causal-Statement Mining (GOAPI / KEGG BRITE / INDRA)
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesSKILL.mdcore27,316 bytesSKILL.mdcore27,316 bytesSKILL.mdcore27,316 bytesSKILL.mdcore27,316 bytespython_implementation.pyruns12,274 bytestest1_genelist.mddoc1,127 bytestest2_protein.mddoc2,674 bytestest3_keyword.mddoc2,812 bytestest4_combined.mddoc5,449 bytes
Read SKILL.md
# Systems Biology & Pathway Analysis
Comprehensive pathway and systems biology analysis integrating multiple curated databases to provide multi-dimensional view of biological systems, pathway enrichment, and protein-pathway relationships.
## When to Use This Skill
**Triggers**:
- "Analyze pathways for this gene list"
- "What pathways is [protein] involved in?"
- "Find pathways related to [keyword/process]"
- "Perform pathway enrichment analysis"
- "Map proteins to biological pathways"
- "Find computational models for [process]"
- "Systems biology analysis of [genes/proteins]"View the complete source on GitHubtooluniverse-target-research
When your agent uses it
Comprehensive drug-target intelligence — tissue expression (GTEx, HPA), pathways, protein interactions (STRING), variant landscape (ClinVar, gnomAD), druggability (DGIdb, ChEMBL approved drugs). 9 parallel research paths with citations. Use for full target profile reports, target characterization for drug discovery, and 'tell me about target X' queries.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
4,538 tokensWhen the skill is activated
Reference documents
27,772 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- When to Use This Skill
- Target Evaluation Reasoning Framework
- Phase 0: Tool Parameter Verification (CRITICAL)
- Critical Workflow Requirements
- Core Strategy: 9 Research Paths
- Identifier Resolution (Phase 1)
- PATH 0: Open Targets Foundation (ALWAYS FIRST)
- PATH 1: Core Identity
- PATH 2: Structure & Domains
- PATH 3: Function & Pathways
- PATH 4: Protein Interactions
- PATH 5: Expression Profile
- PATH 6: Variants & Disease
- PATH 7: Druggability & Target Validation
- PATH 8: Literature & Research (Collision-Aware)
- Retry Logic & Fallback Chains
- Completeness Audit (REQUIRED before finalizing)
- Report Template
- Synthesis: Target Assessment Framework
- Reference Files
Files
EVIDENCE_GRADING.mddoc4,872 bytesEXAMPLES.mddoc22,310 bytesIMPLEMENTATION.mddoc24,756 bytesREFERENCE.mddoc30,410 bytesREPORT_FORMAT.mddoc19,045 bytesSKILL.mdcore19,234 bytesSKILL.mdcore19,234 bytesSKILL.mdcore19,234 bytesSKILL.mdcore19,234 bytes
Read SKILL.md
# Comprehensive Target Intelligence Gatherer
Gather complete target intelligence by exploring 9 parallel research paths. Supports targets identified by gene symbol, UniProt accession, Ensembl ID, or gene name.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Tool parameter verification** - Verify params via `get_tool_info` before calling unfamiliar tools
3. **Evidence grading** - Grade all claims by evidence strength (T1-T4)
4. **Citation requirements** - Every fact must have inline source attribution
5. **Mandatory completeness** - All sections must exist with data minimums or explicit "No data" notes
6. **Disambiguation first** - Resolve all identifiers before research
7. **Negative results documented** - "No drugs found" is data; empty sections are failures
8. **Collision-aware literature search** - Detect and filter naming collisions
9. **English-first queries** - Always use English terms in tool calls, even if the user writes in another language. Translate gene names, disease names, and search terms to English. Only try original-language terms as a fallback if English returns no results. Respond in the user's languageView the complete source on GitHubtooluniverse-toxicology
When your agent uses it
Drug and chemical toxicity assessment via adverse outcome pathways (AOPs), real-world FAERS adverse event signals, FDA labels, toxicogenomic associations, and EPA CompTox quantitative hazard/high-throughput bioactivity data. Triangulates molecular initiating event to cellular outcome to organ-level toxicity to clinical adverse event. Use for hepatotoxicity/cardiotoxicity/nephrotoxicity prediction and toxicology reports.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
4,863 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- KEY PRINCIPLES
- Evidence Grading
- Workflow Overview
- Phase 0: Disambiguation
- Phase 1: Adverse Outcome Pathway Mapping
- Phase 2: Real-World Adverse Event Signals (FAERS)
- Phase 3: FDA Label Safety Mining
- Phase 4: Toxicogenomics (CTD)
- Phase 4c: Quantitative Hazard & HTS Bioactivity (EPA CompTox)
- Phase 4b: Toxin Reference Lookup (T3DB) — currently broken upstream, verified
- Synthesis: Integrated Toxicology Report
- Fallback Chains
- Tool Parameter Reference (Critical)
- Limitations
- References
Files
SKILL.mdcore19,048 bytesSKILL.mdcore19,048 bytes
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# Toxicology Assessment via Adverse Outcome Pathways & Signal Detection
Systematic toxicology analysis that links molecular initiating events (MIEs) through adverse outcome
pathways (AOPs) to apical adverse outcomes, then triangulates with real-world FAERS signals, FDA
label data, and toxicogenomic associations.
## Domain Reasoning
Toxicity has many mechanisms, and the first interpretive question is temporal: is this acute toxicity (immediate effect from a high dose) or chronic toxicity (cumulative damage from long-term low-dose exposure)? Acute and chronic toxicity operate through different mechanisms — acute hepatotoxicity may reflect direct mitochondrial damage, while chronic hepatotoxicity may involve fibrosis from repeated low-level inflammation. They also have different regulatory frameworks: acute toxicity is captured by LD50 and emergency protocols, while chronic toxicity requires long-term carcinogenicity and repeat-dose studies.
## LOOK UP DON'T GUESS
- Adverse outcome pathways for a chemical: query `AOPWiki_list_aops` and `AOPWiki_get_aop`; do not describe mechanisms from memory.
- FAERS adverse event signals: retrieve from `FAERS_count_reactions_by_drug_event` and `FAERS_calculate_disproportionality`; never estimate PRR values.View the complete source on GitHubtooluniverse-use-shared-computer
When your agent uses it
Use tools that run on a colleague's computer (for example models on their lab GPU) or tools other scientists published, through ToolUniverse. Covers redeeming a share code that starts with TU-SHARE-, finding the tools, and running them. Use when the user has a TU-SHARE- code or an invitation, says "use my colleague's model", "run the tool on the lab GPU", or asks what shared or community tools are available.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
591 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Join with a share code (once per computer)
- 2. Find the tools
- 3. Run them
- Leaving
Files
SKILL.mdcore2,587 bytes
Read SKILL.md
# Use a colleague's computer or a published tool
Colleagues can share the tools on their computer, and scientists can publish single tools to ToolUniverse. The tools run on their computer; nothing runs on the user's. The person you are helping is usually a scientist: keep it plain.
## 1. Join with a share code (once per computer)
A colleague sends a code that starts with `TU-SHARE-` (sometimes inside an invitation message). The user redeems it once:
1. Open https://connect.aiscientist.tools/dashboard (sign in if asked).
2. Paste the code into **Use a colleague's computer** and click **Join**.
The code is the colleague's to give; do not post it anywhere else. If the page says the code is invalid or expired, ask the colleague for a new one.
If ToolUniverse runs locally on this computer instead of the hosted service, the same code works with `tu connect TU-SHARE-...`.View the complete source on GitHubtooluniverse-vaccine-design
When your agent uses it
Computational vaccine candidate design: peptide/subunit vaccines via MHC-I/MHC-II epitope prediction (IEDB), population HLA coverage optimization, B-cell epitope identification, and cross-strain conservation analysis. Use for vaccine epitope prediction, HLA allele coverage, multi-epitope construct design, and immunogenicity assessment. Combines predicted MHC binding with experimentally validated IEDB epitopes for higher-confidence designs.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
3,140 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- When to Use
- Core Tools
- Workflow
- Limitations
Scripts executed separately
scripts/population_coverage.py
Files
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Read SKILL.md
# Vaccine Design
Computational pipeline for designing peptide/subunit vaccine candidates through epitope prediction, population coverage optimization, and immunogenicity assessment.
## Reasoning Strategy
Vaccine design requires presenting the right epitopes to elicit protective immunity — not just any immune response, but one that is neutralizing, durable, and broadly applicable. For T-cell vaccines, the core tool is MHC binding prediction (IEDB tools): predict peptide-MHC affinity across multiple HLA alleles, then select epitopes with broad coverage of the target population. For antibody vaccines, prioritize surface-exposed conserved regions — a deeply buried or hypervariable region makes a poor antibody target. MHC binding does not equal immunogenicity; many good binders are not immunogenic in vivo due to tolerance, poor processing, or lack of T-cell help. A multi-epitope strategy (combining MHC-I for CD8+ CTL response, MHC-II for CD4+ helper response, and B-cell epitopes for antibody induction) is more robust than any single epitope. Conservation across pathogen strains is critical — an epitope that mutates under immune pressure (like HIV envelope hypervariable regions) is a poor vaccine target.
**LOOK UP DON'T GUESS**: Do not predict MHC binding or population coverage from memory — use `IEDB_predict_mhci_binding` / `IEDB_predict_mhcii_binding` for T-cell predictions, `IEDB_predict_bcell_epitopes` for antibody (B-cell) epitope prediction, and `iedb_search_epitopes` for validated experimental data. Do not assume what's on the pathogen surface; retrieve annotated sequences from UniProt or BVBRC.
**Key principles**:
1. **Epitope-driven** — vaccines work by presenting epitopes to T/B cells; start with epitope prediction
2. **Population coverage matters** — HLA diversity means no single epitope covers everyone; design for breadth
3. **Multi-epitope is better** — combine CD8+ (MHC-I) and CD4+ (MHC-II) epitopes for robust immunityView the complete source on GitHubtooluniverse-variant-analysis
When your agent uses it
VCF and variant analysis — parsing, annotation, classification (synonymous, missense, frameshift, stop_gained), VAF filtering, coding vs non-coding categorization, multi-condition variant comparison, variant-notation conversion (SPDI/HGVS/VCF/rsID interconversion, ALFA per-ancestry allele frequencies), European Variation Archive (EVA) lookups (per-study cohort variant data, RS-accession resolution), HGVS validation/normalization (Mutalyzer), gene-specific curated variants (LOVD), canonical cross-database allele IDs (ClinGen Allele Registry), automated ACMG/AMP classification (GeneBe), principal-transcript selection (APPRIS), Ensembl linkage disequilibrium (LD) for GWAS/fine-mapping, GRCh37/GRCh38 coordinate liftover and protein/cDNA-to-genomic mapping, and Ensembl ID utilities (stable-ID version history, cross-references, region/gene feature overlap, assembly/species metadata). Use for VCF parsing, variant fraction calculations (denominator = coding subset only, NOT all variants), per-sample mutation profiling, converting a variant between SPDI/HGVS/VCF-coordinate/dbSNP-rsID representations, cross-checking a variant against EVA's raw submitted study cohorts, validating an HGVS string's syntax, looking up curated variants for a well-studied gene, resolving a variant to its ClinGen CA ID, getting an automated ACMG verdict, or picking a gene's principal transcript.
What it loads into context
Frontmatter
328 tokensAlways available to the agent
SKILL.md
12,962 tokensWhen the skill is activated
Reference documents
17,868 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- RULE ZERO — Check for pre-computed results FIRST
- PRIMARY SCRIPTS — use these FIRST
- CRITICAL — Read before writing any code
- Domain Reasoning
- "Proportion classified as benign" — denominator convention
- LOOK UP DON'T GUESS
- CRISPR sgRNA Design Reasoning
- When to Use This Skill
- Core Capabilities
- Workflow Overview
- Phase Summaries
- Common Question Patterns
- ToolUniverse Tools Reference
- Common Use Patterns
- Limitations
- Reference Documentation
- Additional Resources
- Analysis Conventions
Scripts executed separately
python_implementation.pyscripts/annotate_variants.pyscripts/coding_variant_filter.pyscripts/filter_variants.pyscripts/gatk_haplotypecaller_pipeline.pyscripts/parse_vcf.pyscripts/variant_fraction.py
Files
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Read SKILL.md
# Variant Analysis and Annotation
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
---
## PRIMARY SCRIPTS — use these FIRSTView the complete source on GitHubtooluniverse-variant-functional-annotation
When your agent uses it
Functional annotation of protein variants — ProtVar structural/functional context, ClinVar clinical classifications, gnomAD population frequencies, CADD deleteriousness, ClinGen gene-disease validity, plus FAVOR one-call comprehensive GRCh38 annotation. Use for variant annotation pipelines, missense effect prediction, and protein-level variant interpretation with functional context.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
3,375 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Pathogenicity Reasoning Framework
- KEY PRINCIPLES
- Evidence Grading
- Workflow Overview
- Phase 0: Variant Notation Normalization
- Phase 1a: One-Call Comprehensive Annotation (FAVOR)
- Phase 1: ProtVar Protein-Level Annotation
- Phase 2: Population Frequency (gnomAD)
- Phase 3: Deleteriousness Scores (CADD)
- Phase 3b: Multi-Source Annotation (OpenCRAVAT)
- Phase 4: Clinical Classification (ClinVar)
- Phase 5: Gene-Disease Validity (ClinGen)
- Synthesis: Integrated Annotation Report
- Fallback Chains
- Limitations
Files
SKILL.mdcore14,200 bytes
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# Protein Variant Functional Annotation
Comprehensive functional annotation of protein variants by combining ProtVar structural/functional
context, ClinVar clinical classifications, gnomAD population frequencies, CADD deleteriousness
scoring, and ClinGen gene-disease validity.
**Differentiation from tooluniverse-variant-interpretation**: This skill focuses specifically on
**protein-level functional evidence** — structural mapping, residue context, protein domain impact,
and population allele frequencies. It does NOT produce full ACMG classifications or treatment
recommendations. Use `tooluniverse-variant-interpretation` for complete ACMG clinical classification.
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
View the complete source on GitHubtooluniverse-variant-interpretation
When your agent uses it
Clinical variant interpretation from raw variant calls to ACMG-classified recommendations with structural impact analysis. Use for VUS classification, pathogenicity assessment with cited criteria, structure-based variant impact (AlphaFold/PDB), non-coding/regulatory variant effect prediction with sequence deep-learning models (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2), and producing clinical-grade variant reports for return of results or molecular tumor boards. Use this whenever a user asks about a variant's significance, an intronic/promoter/enhancer/UTR non-coding variant's functional impact, or needs ACMG classification — even if they don't say "ACMG".
What it loads into context
Frontmatter
154 tokensAlways available to the agent
SKILL.md
6,494 tokensWhen the skill is activated
Reference documents
22,337 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Triggers
- Key Principles
- LOOK UP, DON'T GUESS
- Workflow Overview
- Phase 1: Variant Identity
- Phase 2: Clinical Databases
- Phase 2.5: Regulatory Context (Non-Coding Only)
- Phase 2.9: Short-Circuit Check
- Phase 3: Computational Predictions
- Phase 4: Structural Analysis (VUS/Novel Missense)
- Phase 4.2: Mechanism of Effect (VUS missense, ESMC-6B SAE)
- Phase 4.5: Expression Context
- Phase 5: Literature Evidence
- Phase 6: ACMG Classification
- Special Scenarios
- Output Structure
- Clinical Recommendations
- Quantified Minimums
- Cross-Skill References
- References
Scripts executed separately
../../../skills/tooluniverse-sequence-analysis/scripts/amino_acids.py
Files
ACMG_CLASSIFICATION.mddoc6,198 bytesCHECKLIST.mddoc8,441 bytesCODE_PATTERNS.mddoc14,286 bytesEXAMPLES.mddoc18,138 bytesSKILL.mdcore25,745 bytesSKILL.mdcore25,745 bytesSKILL.mdcore25,745 bytesTOOLS_REFERENCE.mddoc39,237 bytes../../../skills/tooluniverse-sequence-analysis/scripts/amino_acids.pyruns24,879 bytes
Read SKILL.md
# Clinical Variant Interpreter
Systematic variant interpretation using ToolUniverse - from raw variant calls to ACMG-classified clinical recommendations with structural impact analysis.
## Triggers
Use this skill when users:
- Ask about variant interpretation, classification, or pathogenicity
- Have VCF data needing clinical annotation
- Need ACMG classification for variants
- Want structural impact analysis for missense variants
## Key Principles
View the complete source on GitHubtooluniverse-variant-predictor-dms-validation
When your agent uses it
Validate a variant-effect predictor (AlphaMissense, ESM-C SAE, ESM logits, EVE, conservation scores, or any per-variant numeric score) against experimental deep mutational scanning (DMS) data. Computes per-variant predictor scores, splits variants into neutral vs disruptive groups by DMS effect, runs a Mann-Whitney U test on the predictor scores, and sweeps the stratification thresholds for robustness. Use when you need to know whether a predictor's scores track real functional disruption on a specific protein.
What it loads into context
Frontmatter
131 tokensAlways available to the agent
SKILL.md
5,359 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Required inputs
- Workflow
- Interpretation
- Comparing two predictors
- Honest limitations
- Cross-references
Files
SKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytesSKILL.mdcore20,608 bytes
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# Variant-effect predictor benchmarking against DMS
The core user question: **"I have a variant-effect predictor — does it actually
correlate with experimental DMS measurements on this protein?"**
The predictor can be anything that assigns a numeric score to single missense
variants:
- ESM-C 6B Sparse Autoencoder (SAE) feature drops at the mutation site
- AlphaMissense pathogenicity scores
- ESM logits-based variant scoring (`ESM_score_sequence`)
- EVE / EVE++ scores
- Conservation scores (ConSurf, Rate4Site)
- DynaMut2 ΔΔG predictions
- A custom in-house modelView the complete source on GitHubtooluniverse-variant-to-mechanism
When your agent uses it
End-to-end variant-to-mechanism analysis — trace a variant (rsID/coordinates) through regulatory context, target gene(s), molecular pathway(s), and phenotypic consequences. Integrates 7+ databases across 3 evidence layers (regulatory, molecular, disease) for a mechanistic model. Use for GWAS-hit-to-mechanism, eQTL-causal-gene tracing, and full causal-chain reports.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
4,624 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- When to Use This Skill
- Input Parameters
- Workflow Overview
- Phase 1: Variant Characterization
- Phase 2: Regulatory Context
- Phase 3: Target Gene Identification
- Phase 4: Gene Function & Pathways
- Phase 5: Disease Connection
- Phase 6: Mechanistic Synthesis
- Evidence Grading
- Common Mistakes and Fallbacks
- Reasoning Through Common Scenarios
- Programmatic Access (Beyond Tools)
- Limitations
Files
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# Variant-to-Mechanism Analysis Skill
Trace the full causal chain from a genetic variant to its disease mechanism: regulatory context,
target gene(s), molecular pathways, and phenotypic consequences. Integrates 7+ databases across
3 evidence layers (regulatory, molecular, disease) to build an evidence-graded mechanistic model.
**IMPORTANT**: Always use English terms in tool calls. Respond in the user's language.
---
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
---View the complete source on GitHubsetup-tooluniverse
When your agent uses it
Install and configure ToolUniverse for any use case — MCP server (chat-based), CLI (command line with 14 subcommands), or Python SDK (Coding API with 3 calling patterns). Covers uv/uvx setup, MCP configuration for 12+ AI clients (Cursor, Claude Desktop, Windsurf, VS Code, Codex, Gemini CLI, Trae, Cline, etc.), full CLI reference (tu list/grep/info/find/run/test/status/build/remote/doctor/serve/connect/connections/disconnect), Coding API quickstart, agentic tools, code executor, API key walkthrough, skill installation, and upgrading. Use when user asks how to set up ToolUniverse, which access mode to use (MCP vs CLI vs SDK), configuring MCP servers, using the CLI, troubleshooting installation, upgrading, or mentions installing ToolUniverse or setting up scientific tools. Also triggers for "how do I use ToolUniverse", "what's the best way to access tools", "command line", "tu command", "coding API", "tu build".
What it loads into context
Frontmatter
224 tokensAlways available to the agent
SKILL.md
5,576 tokensWhen the skill is activated
Reference documents
18,541 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Agent Behavior
- Internal Notes (do not show)
- What is ToolUniverse?
- Step 1: Choose How to Use It
- Step 2: Install uv
- CLI Setup
- SDK Setup
- MCP Setup (Chat Mode)
- Step 3: API Keys
- Step 4: Test Together
- Step 5: Install Skills (Recommended for Chat Mode)
- What's Next? (Guided First Use)
- Write Agent Memory
- Team / Project-Level Setup
- Common Issues
- Quick Reference
Scripts executed separately
scripts/check_prerequisites.pyscripts/diagnose_setup.pyscripts/list_tool_categories.pyscripts/verify_installation.py
Files
API_KEYS_REFERENCE.mddoc14,075 bytesCLAUDE_DESKTOP.mddoc6,357 bytesEXAMPLES.mddoc9,435 bytesGITHUB_ISSUE.mddoc2,316 bytesINSTALL.mddoc3,666 bytesMCP_CONFIG.mddoc11,450 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILL.mdcore21,350 bytesSKILLS_CATALOG.mddoc6,062 bytesTRAE.mddoc7,345 bytesTROUBLESHOOTING.mddoc7,714 bytesreferences/mcp-configs.mddoc1,200 bytesscripts/check_prerequisites.pyruns5,477 bytesscripts/diagnose_setup.pyruns11,477 bytesscripts/list_tool_categories.pyruns2,754 bytesscripts/verify_installation.pyruns7,723 bytes
Read SKILL.md
# Setup ToolUniverse
Guide the user step-by-step through setting up ToolUniverse.
## Agent Behavior
- **Detect language** from user's first message. Respond in their language; keep commands/URLs in English.
- Go **one step at a time**. Ask before proceeding.
- Use **AskQuestion** for structured choices.
- **Explain briefly** in plain language. Celebrate small wins.
- When something goes wrong, help troubleshoot before moving on.
## Internal Notes (do not show)
View the complete source on GitHubtooluniverse
When your agent uses it
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...
What it loads into context
Frontmatter
338 tokensAlways available to the agent
SKILL.md
12,146 tokensWhen the skill is activated
Reference documents
5,699 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- FIRST ACTION: Route to a Specialized Skill
- Critical Analysis Conventions
- Routing Table
- Tie-Breaking Rules
- When to Use General Strategies
- Problem-Solving Mode
- Quick Lookup Mode
- Routing Examples
- General Protocols (apply after routing)
Files
.env.templateasset499 bytesCHECKLIST.mddoc2,640 bytesREFERENCE.mddoc13,533 bytesSKILL.mdcore46,057 bytesSKILL.mdcore46,057 bytesSKILL.mdcore46,057 bytesSKILL.mdcore46,057 bytesSKILL.mdcore46,057 bytesreferences/general-strategies.mddoc5,414 bytes
Read SKILL.md
# 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:**
1. 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)
2. Find the matching keyword row in the Routing Table
3. Call `Skill(skill="<skill-name>")` immediately
4. 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.
View the complete source on GitHubtooluniverse-acmg-variant-classification
When your agent uses it
Systematic ACMG/AMP germline variant classification with all 28 criteria (PVS1, PS1-4, PM1-6, PP1-5, BA1, BS1-4, BP1-7) for clinical significance. Produces 5-tier verdict (Pathogenic / Likely Pathogenic / VUS / Likely Benign / Benign) with cited evidence per criterion. Use for variant interpretation, VUS resolution, and pathogenicity assessment. Combines ClinVar, gnomAD, computational predictors, and gene-mechanism context.
What it loads into context
Frontmatter
133 tokensAlways available to the agent
SKILL.md
4,706 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- ACMG Reasoning
- LOOK UP, DON'T GUESS
- When to Use
- Tool Parameter Reference
- Phase 0: Variant Validation and Normalization
- Phase 1: Population Frequency (BA1, BS1, BS2, PM2)
- Phase 2: Computational Predictions (PP3, BP4)
- Phase 3: Clinical Database Evidence (PS1, PM5, PP5, BP6)
- Phase 4: Functional Domain and Protein Analysis (PM1, PP2, BP1)
- Phase 5: Splice Impact Assessment (PVS1)
- Phase 6: Literature and Functional Evidence (PS3, BS3, PP1, PP4)
- Automated Cross-Check (InterVar)
- Classification Algorithm
- Output Format
- Common Patterns
Files
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# ACMG/AMP Variant Classification
## ACMG Reasoning
Each criterion (PS, PM, PP for pathogenic; BS, BP for benign) contributes a weighted piece of evidence for or against pathogenicity. The classification is the COMBINATION of all activated criteria, not any single criterion. Do not overweight a single finding.
The hierarchy is: PVS1 (very strong) > PS (strong) > PM (moderate) > PP (supporting). On the benign side: BA1 (stand-alone) > BS (strong) > BP (supporting). A frameshift in a LOF-intolerant gene (PVS1) plus a ClinVar expert-panel pathogenic entry (PS1) is pathogenic. A single PP criterion alone is not. The combination rule is what matters.
Two common errors to avoid: (1) seeing a "Pathogenic" ClinVar entry and stopping — that is PP5 (supporting) unless it has expert-panel review, not automatic confirmation; (2) dismissing a variant because one predictor says "tolerated" — discordant predictors mean neither PP3 nor BP4 applies, which is neutral evidence, not benign evidence.
Always apply criteria conservatively. When evidence is ambiguous, leave the criterion unmet. Cite the source for every criterion you activate so clinicians can audit the reasoning.
**KEY PRINCIPLES**:
1. **Criteria-driven** — cite which criteria were activated and whyView the complete source on GitHubtooluniverse-admet-prediction
When your agent uses it
Comprehensive ADMET (Absorption, Distribution, Metabolism, Excretion, Toxicity) profiling for drug candidates. Integrates ADMET-AI predictions, SwissADME drug-likeness, PubChemTox experimental toxicity, ChEMBL clinical data, Lipinski rule-of-five, and CYP interaction data. Use for drug-likeness assessment, BBB penetration, bioavailability, hepatotoxicity prediction, ADME/PK profiling, or screening compound libraries before lab testing.
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
4,413 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Before You Run
- COMPUTE, DON'T DESCRIBE
- KEY PRINCIPLES
- Evidence Grading
- Workflow: 5-Phase ADMET Profiling
- Completeness Checklist (MANDATORY before reporting)
Files
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# ADMET Prediction & Drug Candidate Profiling
**ADMET reasoning**: a drug fails if it can't be absorbed, distributes to wrong tissues, isn't metabolized safely, or isn't excreted. Evaluate each property independently — good absorption doesn't compensate for liver toxicity. The ADME properties determine whether a compound reaches its target at therapeutic concentrations; toxicity determines whether it's safe to do so. Prioritize experimental data (T2) over computational predictions (T3) — ADMETAI predictions are screening tools, not definitive verdicts. When a FAIL is flagged in any toxicity category (hERG, AMES, DILI), treat it as program-limiting until wet-lab data refutes it.
**LOOK UP DON'T GUESS**: never assume SMILES, CID, or experimental LD50 values — always call PubChem to resolve compound identity before any ADMETAI or PubChemTox call.
Comprehensive pharmacokinetic and toxicity profiling integrating AI-based ADMET predictions, rule-based drug-likeness filters, and experimental benchmarks from curated databases.
## When to Use This Skill
**Triggers**:
- "What are the ADMET properties of [compound]?"
- "Is [drug] likely to cross the blood-brain barrier?"
- "Predict the toxicity of this SMILES: ..."View the complete source on GitHubtooluniverse-adverse-event-detection
When your agent uses it
Detect and analyze adverse drug event signals using FDA FAERS reports, drug labels, and disproportionality statistics (PRR, ROR, IC). Generates quantitative safety signal scores (0-100) with evidence grading. Use for post-market surveillance, pharmacovigilance, drug safety assessment, regulatory submissions, and detecting rare AE signals not visible in clinical trials.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
2,980 tokensWhen the skill is activated
Reference documents
8,790 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Workflow Overview
- Phase Summaries
- Edge Cases
Files
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Adverse Drug Event Signal Detection & Analysis
Automated pipeline for detecting, quantifying, and contextualizing adverse drug event signals using FAERS disproportionality analysis, FDA label mining, mechanism-based prediction, and literature evidence. Produces a quantitative Safety Signal Score (0-100) for regulatory and clinical decision-making.
**KEY PRINCIPLES**:
1. **Signal quantification first** - Every adverse event must have PRR/ROR/IC with confidence intervals
2. **Serious events priority** - Deaths, hospitalizations, life-threatening events always analyzed first
3. **Multi-source triangulation** - FAERS + FDA labels + OpenTargets + DrugBank + literature
4. **Context-aware assessment** - Distinguish drug-specific vs class-wide vs confounding signals
5. **Report-first approach** - Create report file FIRST, update progressively
6. **Evidence grading mandatory** - T1 (regulatory/boxed warning) through T4 (computational)View the complete source on GitHubtooluniverse-adverse-outcome-pathway
When your agent uses it
Map environmental and industrial chemicals to adverse outcome pathways (AOPs) — molecular initiating event to organ-level toxicity. Uses AOPWiki, GHS classification, IARC carcinogen status, and LD50 data. Use for environmental/industrial chemical risk assessment, regulatory-grade hazard characterization, and AOP stressor mapping. Distinct from drug-safety analysis (use tooluniverse-pharmacovigilance for drugs).
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
2,474 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Key Tools
- Workflow
- Tool Parameter Reference
- Common Patterns
- Reasoning Framework for Result Interpretation
- Fallback Chains
Files
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# Adverse Outcome Pathway & Regulatory Risk Assessment
Distinct from drug safety (see tooluniverse-toxicology): this skill targets **environmental and
industrial chemicals** where the focus is AOP stressor mapping, GHS classification, LD50 hazard
quantification, and IARC carcinogen status — not FAERS signals or FDA drug labels.
## When to Use
Apply when researcher asks about:
- "What AOPs are associated with [pesticide/solvent/industrial chemical]?"
- "What is the GHS hazard classification for [compound]?"
- "What is the LD50 for [compound]?"
- "Is [compound] a carcinogen (IARC classification)?"
- "Which genes does [chemical] interact with (CTD)?"View the complete source on GitHubtooluniverse-aging-senescence
When your agent uses it
Aging biology, cellular senescence, and longevity research. Covers senescence markers (p16/CDKN2A, SASP, SA-beta-gal), aging hallmarks, senolytic drug discovery (dasatinib+quercetin, fisetin, navitoclax), epigenetic clocks, telomere biology, and longevity GWAS. Use for senescence-pathway analysis, age-related disease genetics, senolytic-target discovery, and centenarian-genetics queries. Distinguishes correlative vs causal evidence (knockout, intervention).
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
2,952 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Aging Research Reasoning
- LOOK UP, DON'T GUESS
- When to Use
- Workflow
- Phase 1: Hallmarks Classification
- Phase 2: Genetic Evidence
- Phase 3: Pathway Analysis
- Phase 4: Senolytic and Geroprotector Drug Discovery
- Phase 5: Literature and Clinical Context
- Phase 6: Report Structure
- Age-Dependent Expression Analysis
- Limitations
Files
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# Aging & Cellular Senescence Research
## Aging Research Reasoning
Before querying any tool, ask the central question: is this a cause or consequence of aging?
Senescence markers (SA-β-gal, p16/CDKN2A, SASP factors like IL-6 and IL-8) indicate that senescent cells are present. But their presence does not prove that senescence is driving the phenotype. Correlation is easy to establish. Causation requires an intervention. If senolytic drugs (dasatinib+quercetin, fisetin, navitoclax) clear senescent cells and the age-related phenotype improves, that is causal evidence. If clearing senescent cells has no effect, something else is driving the pathology.
Apply this reasoning when interpreting any gene or pathway query: classify it first by hallmark, then ask whether the evidence for its role is correlative (expression data, GWAS association) or causal (functional assay, genetic knockout, senolytic intervention).
Evidence grade the findings: T1 is human genetic evidence (GWAS, centenarian studies). T2 is model organism lifespan data. T3 is cell culture senescence data. T4 is computational prediction. Do not conflate T3 cell culture data with T1 human evidence — they are very different levels of confidence.
A final principle: cellular senescence is one hallmark of aging, not aging itself. Distinguish senescence from organismal aging, from age-related disease, and from progeria (accelerated aging syndromes). These require different tools and different interpretations.
View the complete source on GitHubtooluniverse-antibody-engineering
When your agent uses it
Therapeutic antibody engineering and optimization, lead-to-clinical-candidate. Covers sequence humanization (germline alignment, framework retention), affinity maturation, developability (aggregation, stability, PTMs), structure modeling (AlphaFold/PDB CDR analysis), immunogenicity prediction, and manufacturing feasibility. Use for biologic-drug optimization, mAb design review, biosimilar engineering, and clinical-precedent comparison.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
3,891 tokensWhen the skill is activated
Reference documents
27,709 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- When to Use
- Critical Workflow Requirements
- Phase 0: Tool Verification
- Workflow Overview
- Phase 1: Input Analysis & Characterization
- Phase 2: Humanization Strategy
- Phase 3: Structure Modeling & Analysis
- Phase 4: Affinity Optimization
- Phase 5: Developability Assessment
- Phase 6: Immunogenicity Prediction
- Phase 7: Manufacturing Feasibility
- Phase 8: Final Report & Recommendations
- Tool Reference
- Reference Files
Scripts executed separately
scripts/developability.py
Files
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# Antibody Engineering & Optimization
AI-guided antibody optimization pipeline from preclinical lead to clinical candidate. Covers sequence humanization, structure modeling, affinity optimization, developability assessment, immunogenicity prediction, and manufacturing feasibility.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create optimization report before analysis
2. **Evidence-graded humanization** - Score based on germline alignment and framework retention
3. **Developability-focused** - Assess aggregation, stability, PTMs, immunogenicity
4. **Structure-guided** - Use AlphaFold/PDB structures for CDR analysis
5. **Clinical precedent** - Reference approved antibodies for validation
6. **Quantitative scoring** - Developability score (0-100) combining multiple factors
7. **English-first queries** - Always use English terms in tool calls, even if user writes in another language. Respond in user's language
---View the complete source on GitHubtooluniverse-antigravity-plugin
When your agent uses it
Install, set up, verify, update, pin, uninstall, or troubleshoot the ToolUniverse plugin on Google Antigravity (AGY / Antigravity IDE / Antigravity 2.0). ALWAYS consult this skill for any of those — don't answer from memory, because the exact CLI name (agy), the "agy plugin install <path>" flow, the uvx tooluniverse MCP server, and the API-key env vars are easy to get wrong. Use it whenever someone wants to get ToolUniverse (or "the 1000+ scientific tools" / "the harvard tools") working on Antigravity, says the Antigravity plugin or its tools/skills won't load, hits a uvx or MCP-server startup error, asks how Antigravity updates it, wants to pin or remove it, or finds it running an old tool version. Not for the Codex plugin (use tooluniverse-codex-plugin) or Claude Code plugin (use tooluniverse-claude-code-plugin), for running research with the tools, or for authoring new tools or skills.
What it loads into context
Frontmatter
237 tokensAlways available to the agent
SKILL.md
1,018 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites (check once)
- Install
- Verify it worked
- What you get
- API keys (optional, but recommended)
- Update
- Troubleshooting
Files
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# Install the ToolUniverse Plugin for Google Antigravity (AGY)
One-step install on Google Antigravity (AGY / Antigravity IDE / Antigravity 2.0): an MCP server exposing 1000+ scientific tools plus 120+ research skills — all auto-configured.
## Prerequisites (check once)
```bash
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh
agy --version # Antigravity CLI; if missing: https://antigravity.google
```
## Install
In Antigravity, plugins can be installed using the `agy` CLI:View the complete source on GitHubtooluniverse-binder-discovery
When your agent uses it
Discover novel small-molecule binders for protein targets using structure-based and ligand-based screening. Covers druggability assessment, known-ligand mining (ChEMBL, BindingDB), similarity expansion, ADMET filtering, and synthesis feasibility. Use for hit identification, virtual screening, target-to-compounds workflows, and lead-finding before commit-to-medchem.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
3,848 tokensWhen the skill is activated
Reference documents
26,295 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Binding Site Reasoning (Start Here)
- Critical Workflow Requirements
- Workflow Overview
- Phase 0: Tool Verification
- Phase 1: Target Validation
- Phase 2: Known Ligand Mining
- Phase 3: Structure Analysis
- Phase 4: Compound Expansion
- Phase 5: ADMET Filtering
- Phase 6: Candidate Docking & Prioritization
- Phase 6.5: Literature Evidence
- Fallback Chains
- Programmatic Access (Beyond Tools)
- NVIDIA NIM Runtime Notes
- Reference Files
Files
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# Small Molecule Binder Discovery Strategy
Systematic discovery of novel small molecule binders using 60+ ToolUniverse tools across druggability assessment, known ligand mining, similarity expansion, ADMET filtering, and synthesis feasibility.
**LOOK UP DON'T GUESS** - Always retrieve actual data from tools before drawing conclusions. Do not assume druggability, binding sites, or compound properties based on target class alone.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Target validation FIRST** - Confirm druggability before compound searching
3. **Multi-strategy approach** - Combine structure-based and ligand-based methods
4. **ADMET-aware filtering** - Eliminate poor compounds early
5. **Evidence grading** - Grade candidates by supporting evidence
6. **Actionable output** - Provide prioritized candidates with rationale
7. **English-first queries** - Always use English terms in tool calls. Respond in the user's languageView the complete source on GitHubtooluniverse-bioinformatics-resource-discovery
When your agent uses it
Discover which bioinformatics software tool, biomedical REST API, R/Bioconductor package, CRAN package, or training material exists for a task, before writing analysis code or building a pipeline. Covers ELIXIR bio.tools (30,000+ software tools/databases by keyword, EDAM topic, or EDAM operation), SmartAPI (biomedical API registry, incl. NCATS Translator components), ELIXIR TeSS (bioinformatics training materials/events), Bioconductor (R packages for computational biology), and CRAN (general R packages). Use when someone asks 'is there an existing tool/package for X', 'what software does [analysis]', 'find an API for Y', or 'is there a tutorial on Z'.
What it loads into context
Frontmatter
165 tokensAlways available to the agent
SKILL.md
2,570 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- 1. bio.tools — Bioinformatics Software & Database Registry
- 2. SmartAPI — Biomedical API Registry
- 3. ELIXIR TeSS — Training Materials & Events
- 4. Bioconductor — R Packages for Computational Biology
- 5. CRAN — General R Package Registry
- Choosing Among the Five Resources
- Common Pitfalls
Files
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# Bioinformatics Resource Discovery
Answers "what already exists for this task" — software, APIs, R packages, or training material — **before** writing new analysis code, choosing an nf-core pipeline, or wrapping a new REST API by hand.
**LOOK UP, DON'T GUESS**: Never assert that a tool/package/API does or doesn't exist for a task from memory. These 5 registries are live and current — search them.
## When to Use This Skill
- "Is there existing software for [bioinformatics task]?" -> bio.tools
- "What biomedical APIs exist for [domain], and where's the base URL/spec?" -> SmartAPI
- "Is there a tutorial/course on [technique]?" -> ELIXIR TeSS
- "What R package does [analysis] in Bioconductor?" -> Bioconductor
- "What does CRAN package X do / what version / what does it depend on?" -> CRAN
View the complete source on GitHubtooluniverse-biomedical-fact-lookup
When your agent uses it
Answer biomedical FACTUAL / recall / multiple-choice questions by querying ToolUniverse database tools instead of answering from memory. Triggers on any 'which gene/drug/variant/disease/pathway/miRNA/TF...' lookup, any question phrased 'according to <database>' (DisGeNet, OMIM, MSigDB, miRDB, GTRD, MGI, Ensembl, ClinVar, ChEMBL, OpenTargets, Reactome, GtoPdb, UniProt...), and multiple-choice biology/medicine knowledge questions where one option must be verified against an authoritative source. NOT for analyzing user-supplied data files (CSV/VCF/h5ad → use the data-analysis router) and NOT for open-ended literature synthesis. Use whenever a single correct answer exists in a public biomedical database and could be looked up rather than guessed.
What it loads into context
Frontmatter
246 tokensAlways available to the agent
SKILL.md
7,357 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Six traps that produce a confidently wrong answer
- RULE ZERO: Look it up, never guess
- Multiple-choice procedure
- Routing table — question pattern → tool
- MSigDB set-name conventions (the most common LAB-Bench pattern)
- Gene–disease "in database X but NOT database Y" recipe
- Drug choice for a described patient (clinical vignette)
- Mouse-phenotype matching (MGI) — fallback only
- Computational procedures (when the answer is COMPUTED, not looked up)
- Interpretation
- Limitations (honest)
Files
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# Biomedical Fact Lookup (tool-grounded answering)
Factual biomedical questions — "which gene is in set X", "which gene is associated with disease Y according to DisGeNet", "which gene has a TF binding site per GTRD" — have an authoritative answer in a public database. Guessing from memory is unreliable (≈chance on niche annotations); the matching ToolUniverse tool returns the ground truth.
## Six traps that produce a confidently wrong answer
Each was observed producing a wrong answer on a real question. Check them before
answering; the detail for each is further down.
1. **"Highest p-value" in GWAS means most significant** — the *smallest* number.
Read literally it picks the study's weakest hit (`rs2476491` at 1e-06 instead
of `rs7775055-G` at 3e-174).
2. **A window of "N bp upstream plus M bp downstream" spans N+M+1 bases** — theView the complete source on GitHubtooluniverse-cancer-classification
When your agent uses it
Translate free-text tumor descriptions to OncoTree codes and resolve cancer subtypes/tissue hierarchy. Cross-references UMLS/NCI vocabularies. Use for standardizing cancer-type nomenclature in EHR free-text, building cohorts in OncoKB or GDC, mapping tumor-board notes to ontology codes, and ensuring consistent terminology across cancer-genomics pipelines.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
2,896 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Key Tools
- Workflow
- Tool Parameter Reference
- Common OncoTree Codes (verified working)
- Common Patterns
- Tumor Classification Reasoning (CRITICAL)
- Reasoning Framework for Result Interpretation
- Fallback Chains
Files
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# Cancer Classification via OncoTree
Standardize cancer type nomenclature using the OncoTree ontology. Resolves free-text tumor
descriptions to structured codes with UMLS/NCI cross-references, enabling downstream use in
OncoKB variant annotation and GDC cohort selection.
## When to Use
Apply when researcher asks about:
- "What is the OncoTree code for [tumor description]?"
- "Find all subtypes of [cancer type]"
- "What cancers originate in [tissue]?"
- "I need the tumor type code for OncoKB annotation"
- "What is the TCGA/COSMIC code for [cancer]?"View the complete source on GitHubtooluniverse-cancer-genomics-tcga
When your agent uses it
TCGA/GDC cancer genomics analysis — cohort construction, clinical metadata retrieval, somatic mutation frequencies, survival analysis, and multi-omics integration. Use for TCGA-BRCA-style cohort studies, mutation prevalence by cancer type, survival-by-mutation analysis, and pan-cancer driver discovery. Always cancer-type-specific (don't use pan-cancer counts without cohort context).
What it loads into context
Frontmatter
183 tokensAlways available to the agent
SKILL.md
4,242 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- NOT for (use other skills instead)
- Workflow Overview
- Key Identifiers
- Phase 1: Study Selection
- Phase 2: Clinical Data
- Phase 3: Somatic Mutations
- Phase 4: CNV Analysis (Progenetix)
- Phase 5: Survival Analysis
- Phase 6: Variant Interpretation (OncoKB)
- Tool Quick Reference
- Example Workflows
- Reasoning Framework
- Programmatic Access (Beyond Tools)
- Limitations
Files
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# Cancer Genomics / TCGA Analysis
**TCGA analysis starts with: what cancer type? what data type?** Build your cohort FIRST (GDC filters), then analyze. Don't query mutations without defining the cohort — pan-cancer counts from `GDC_get_mutation_frequency` are uninformative without cancer-type context. A mutation frequency of 10% in one cancer type may be 0.5% in another; always specify `project_id`. Survival analysis (Kaplan-Meier) is hypothesis-generating in retrospective TCGA data — always report sample size and p-value, and note that TCGA cohorts are not treatment-stratified.
**LOOK UP DON'T GUESS**: never assume TCGA project IDs, NCIt codes, or gene coordinates — use `GDC_list_projects` to confirm project IDs and `Progenetix_list_filtering_terms` for NCIt codes.
Systematic TCGA/GDC analysis: define cohorts, retrieve clinical data, profile somatic
mutations, query copy number variations, run survival analysis, and interpret variants
with OncoKB.
## When to Use
- "What is the mutation frequency of TP53 in TCGA-BRCA?"
- "Get survival data for TCGA-LUAD patients"View the complete source on GitHubtooluniverse-cancer-variant-interpretation
When your agent uses it
Clinical interpretation of somatic cancer mutations for precision oncology. Transforms a gene + variant + cancer-type input into an actionable report: clinical evidence tier (CIViC, OncoKB), therapeutic options (FDA-approved + investigational), resistance mechanisms, prognosis, and matching clinical trials. Use for tumor-board variant calls, somatic-mutation actionability assessment, and treatment selection. Always cancer-type-specific.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
4,541 tokensWhen the skill is activated
Reference documents
15,363 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parsing
- Phase 0: Tool Parameter Verification (CRITICAL)
- Workflow Overview
- Clinical Reasoning Strategies
- Tool Reference (Verified Parameters)
- Fallback Strategy
Files
ANALYSIS_DETAILS.mddoc14,487 bytesEXAMPLES.mddoc7,095 bytesINPUT_PARSING.mddoc1,871 bytesQUICK_START.mddoc4,794 bytesREPORT_TEMPLATE.mddoc5,033 bytesSCORING_TABLES.mddoc4,400 bytesSKILL.mdcore18,469 bytesSKILL.mdcore18,469 bytesSKILL.mdcore18,469 bytesTOOLS_REFERENCE.mddoc19,228 bytes
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# Cancer Variant Interpretation for Precision Oncology
Comprehensive clinical interpretation of somatic mutations in cancer. Transforms a gene + variant input into an actionable precision oncology report covering clinical evidence, therapeutic options, resistance mechanisms, clinical trials, and prognostic implications.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Evidence-graded** - Every recommendation has an evidence tier (T1-T4)
3. **Actionable output** - Prioritized treatment options, not data dumps
4. **Clinical focus** - Answer "what should we treat with?" not "what databases exist?"
5. **Resistance-aware** - Always check for known resistance mechanisms
6. **Cancer-type specific** - Tailor all recommendations to the patient's cancer type when provided
7. **Source-referenced** - Every statement must cite the tool/database source
8. **English-first queries** - Always use English terms in tool calls (gene names, drug names, cancer types), even if the user writes in another language. Respond in the user's language
View the complete source on GitHubtooluniverse-cell-line-profiling
When your agent uses it
Cancer cell-line selection and profiling for experimental model choice. Cross-references DepMap, Cellosaurus, COSMIC, PharmacoDB to deliver identity verification, mutation/CNV profile, gene dependencies, drug sensitivities, and druggable targets. Use to answer 'which cell line should I use for studying gene X?' or 'is this cell line a good model for cancer Y?'. Outputs ranked recommendations with rationale, growth characteristics, and known pitfalls.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
5,107 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Phase 0: Tool Parameter Reference (CRITICAL)
- Workflow Overview
- Phase 1: Cell Line Identification
- Phase 2: Molecular Profiling
- Phase 3: Gene Dependencies (CRISPR Screens)
- Phase 4: Drug Sensitivity
- Phase 5: Target Druggability and Recommendations
- Common Use Patterns
- Quick Reference: Common Cancer Cell Lines by Type
- Cross-Referencing Cell Line IDs
- Error Handling
- Completeness Checklist
Scripts executed separately
scripts/depmap_gene_dependency.py
Files
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# Cancer Cell Line Profiling and Selection
Comprehensive profiling of cancer cell lines for experimental model selection. Transforms a query (cancer type, gene, or cell line name) into an actionable report covering identity verification, molecular features, gene dependencies, drug sensitivities, and druggable targets.
**KEY PRINCIPLES**:
1. **Decision-first** - Answer "which cell line should I use?" not "here is all the data"
2. **Multi-source validation** - Cross-reference DepMap, Cellosaurus, COSMIC, PharmacoDB
3. **Actionable output** - Ranked cell line recommendations with rationale
4. **Practical focus** - Include availability, growth characteristics, common pitfalls
5. **Gene-aware** - When a gene of interest is given, prioritize lines with relevant mutations/dependencies
6. **Source-referenced** - Cite database sources for every claim
7. **English-first queries** - Always use English terms in tool calls, even if the user writes in another language
## LOOK UP, DON'T GUESSView the complete source on GitHubtooluniverse-chemical-compound-retrieval
When your agent uses it
Retrieve chemical compound data from PubChem and ChEMBL with disambiguation, cross-referencing, and stereochemistry handling. Use for resolving compound names to SMILES/InChI/CID/ChEMBL IDs (including OPSIN deterministic IUPAC-name-to-structure parsing), fetching molecular properties, distinguishing isomers/stereo forms, and cross-validating identity across databases. Always use English compound names; flags ambiguous queries (e.g., Vitamin D has multiple forms).
What it loads into context
Frontmatter
112 tokensAlways available to the agent
SKILL.md
2,212 tokensWhen the skill is activated
Reference documents
2,039 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning: Disambiguation
- Workflow
- Fallback Chains
- Evidence Grading
- SMILES Verification
- Identifier Resolution and Cross-Database Referencing
- Cheminformatics Calculations and Structure Visualization
- Tool Reference
Files
CHECKLIST.mddoc1,257 bytesSKILL.mdcore8,221 bytesSKILL.mdcore8,221 bytesSKILL.mdcore8,221 bytesSKILL.mdcore8,221 bytesexamples.mddoc6,111 bytes
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# Chemical Compound Information Retrieval
Retrieve comprehensive chemical compound data with proper disambiguation and cross-database validation.
**LOOK UP DON'T GUESS**: Never assume a CID, ChEMBL ID, or molecular property value. Always retrieve from PubChem/ChEMBL.
**English-first**: Always use English compound names in tool calls. Respond in user's language.
## Domain Reasoning: Disambiguation
"Aspirin" = one compound. "Vitamin D" = multiple forms (D2/D3/active metabolite). For generic class names (steroids, vitamins, acids), present candidates and confirm before proceeding.
---
View the complete source on GitHubtooluniverse-chemical-safety
When your agent uses it
Chemical safety and toxicology assessment integrating ADMET-AI predictions, CTD toxicogenomics, PubChemTox experimental data, GHS/IARC hazard classification, and exposure-context analysis. Use for chemical hazard identification, occupational/consumer-product toxicity, dose-response evaluation, and acute (LD50) vs chronic toxicity assessment. Distinguishes drug toxicity from environmental chemical toxicity.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
3,558 tokensWhen the skill is activated
Reference documents
6,105 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- COMPUTE, DON'T DESCRIBE
- KEY PRINCIPLES
- Evidence Grading System (MANDATORY)
- Core Strategy: 8 Research Phases
- Tool Summary by Phase
- Risk Classification Matrix
- Report Structure
- Mandatory Completeness Checklist
- Common Use Patterns
- Limitations
- Reference Files
- Summary
Files
SKILL.mdcore14,138 bytesSKILL.mdcore14,138 bytesevidence-grading.mddoc1,620 bytesphase-details.mddoc7,593 bytesphase-procedures-detailed.mddoc8,814 bytesreport-templates.mddoc4,442 bytes
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# Chemical Safety & Toxicology Assessment
**Toxicity assessment**: identify the chemical, check known hazards (GHS, IARC), then look for ADMET predictions. Dose makes the poison — always consider exposure level, as a compound that is toxic at high doses may be safe at relevant exposures. Distinguish between acute toxicity (LD50, GHS category) and chronic hazards (carcinogenicity, endocrine disruption) — they require different risk management approaches. Computational predictions (ADMETAI) are T3 evidence and must be anchored by experimental data from PubChemTox or FDA labels wherever available. When evidence conflicts between prediction and experiment, always defer to the experimental finding.
**LOOK UP DON'T GUESS**: never assume GHS categories, IARC classification, or CTD disease links — always call PubChemTox and CTD tools to retrieve current classifications before reporting.
Comprehensive chemical safety analysis integrating predictive AI models, curated toxicogenomics databases, regulatory safety data, and chemical-biological interaction networks.
## When to Use This Skill
**Triggers**:
- "Is this chemical toxic?" / "Assess the safety profile of [drug/chemical]"
- "What are the ADMET properties of [SMILES]?"
- "What genes does [chemical] interact with?" / "What diseases are linked to [chemical] exposure?"View the complete source on GitHubtooluniverse-chemical-sourcing
When your agent uses it
Find commercial sources for chemical compounds — PubChem/ChEMBL identity resolution then vendor catalog search across ZINC, Enamine, eMolecules, Mcule. Compares pricing, availability, and identifies purchasable analogs when an exact compound is not in stock. Use for chemical procurement, virtual library curation, and 'where can I buy X' questions for synthesis planning.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
3,097 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Databases
- Workflow Overview
- Phase Details
- Common Analysis Patterns
- Edge Cases & Fallbacks
- Interpretation Framework
- Limitations
Files
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# Chemical Compound Sourcing & Procurement
Pipeline for identifying, sourcing, and purchasing chemical compounds from commercial vendors. Resolves compound identity through PubChem/ChEMBL, searches multiple vendor databases (ZINC, Enamine, eMolecules, Mcule), compares pricing and availability, and identifies purchasable analogs when exact compounds are unavailable.
**Guiding principles**:
1. **Identity first** -- confirm the compound's structure (SMILES, InChI) before searching vendors; names can be ambiguous
2. **Multi-vendor comparison** -- always check multiple sources; pricing and stock vary significantly
3. **Analog fallback** -- if the exact compound is unavailable, search for close analogs
4. **Purity and quantity awareness** -- note catalog purity grades and minimum order quantities
5. **Structure over name** -- vendor searches by SMILES/InChI are more reliable than name searches
6. **English-first queries** -- use English compound names in tool calls
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory. A database-verified answer is always more reliable than a guess.View the complete source on GitHubtooluniverse-clinical-data-integration
When your agent uses it
End-to-end drug safety review integrating FDA labels, FAERS adverse event reports, PRR/ROR disproportionality, pharmacogenomic biomarkers, clinical trial data, and published literature. Use for regulatory drug safety reviews, comprehensive pharmacovigilance reports, label-vs-real-world AE comparison, and clinical decision support for drug safety.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
3,460 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Data Sources
- Workflow Overview
- Phase Details
- Common Analysis Patterns
- Edge Cases & Fallbacks
- Limitations
Files
SKILL.mdcore14,380 bytesSKILL.mdcore14,380 bytes
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# Clinical Data Integration for Drug Safety
End-to-end drug safety review pipeline that integrates FDA label information, FAERS spontaneous reports, disproportionality signal detection, pharmacogenomic biomarkers, clinical trial data, and published literature. Designed for regulatory assessments, pharmacovigilance, and clinical decision support.
**Guiding principles**:
1. **Label is ground truth** -- FDA-approved labeling is the authoritative starting point for known safety information
2. **Signals need context** -- a FAERS signal without label or literature corroboration is hypothesis-generating, not confirmatory
3. **Disproportionality is not causation** -- PRR/ROR measure reporting patterns, not causal relationships
4. **Pharmacogenomics narrows risk** -- PGx biomarkers can identify which patients face elevated risk
5. **Progressive reporting** -- create the report file early; update section by section
6. **English-first queries** -- use English drug names in all tool calls; respond in the user's language
Clinical data integration starts with data harmonization. Different hospitals code the same diagnosis differently (ICD-10 vs SNOMED). Before merging datasets, verify the coding system. Missing data is informative — a missing lab value may mean the test wasn't ordered (patient was stable) not that the result was normal.
View the complete source on GitHubtooluniverse-clinical-guidelines
When your agent uses it
Search and retrieve clinical practice guidelines from 12+ authoritative sources — NICE, WHO, NCCN, AHA, ADA, SIGN, USPSTF, IDSA, NIH consensus, ESMO/ESC/EASL European societies, and US specialty associations. Use for evidence-graded treatment recommendations, dosing protocols, screening guidance, and authoritative-source-prioritized clinical guidance (NICE/WHO ranked above society guidelines).
What it loads into context
Frontmatter
100 tokensAlways available to the agent
SKILL.md
4,719 tokensWhen the skill is activated
Reference documents
2,947 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Guideline Hierarchy
- COMPUTE, DON'T DESCRIBE
- Search Strategy
- Diagnostic Test Selection Reasoning
- Lab Test Interpretation Strategy
- Surgical Decision Making
- Applying Guidelines to Patients
- Tool Workflow
- Evidence Grading Quick Reference
- Fallback Strategy
- Synthesis Template
- Known Limitations
- Clinical MCQ Reasoning Framework
Files
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# Clinical Guidelines Search & Retrieval
## Guideline Hierarchy
Not all guidelines carry equal weight. Evaluate sources in this order:
1. **NICE and WHO** — Evidence-graded, regularly updated, rigorous systematic review process. NICE guidelines include explicit recommendation strength (e.g., "offer" vs "consider").
2. **Society guidelines (AHA, ADA, NCCN, SIGN)** — Expert-consensus panels within a specialty. May lag behind the latest evidence by 1-3 years. Strong within their domain but narrower scope.
3. **Aggregator databases (TRIP, OpenAlex)** — Index guidelines from multiple societies. Good for breadth and discovery, but you must verify the original source.
4. **Literature databases (PubMed, EuropePMC)** — Return guideline-related publications, not curated guideline text. Useful as a fallback, not a primary source.
**Always check publication date.** A 2015 guideline may be superseded by a 2024 update. When presenting results, include the year prominently and note if newer guidance may exist.
---View the complete source on GitHubtooluniverse-clinical-risk-scoring
When your agent uses it
Compute and interpret validated bedside clinical risk scores and pretest probabilities for an INDIVIDUAL patient — pick the right score for the scenario, gather inputs, run the deterministic calculator tool, and read the result against an interpretation table. Covers CHA2DS2-VASc (AF stroke risk), HAS-BLED (bleeding on anticoagulation), CURB-65 (pneumonia severity / admit decision), qSOFA (sepsis screen), Child-Pugh + MELD-Na (cirrhosis severity / transplant priority), Wells DVT and Wells PE (VTE pretest probability), ASCVD (10-year cardiovascular risk / statin decision), and eGFR CKD-EPI (kidney function / drug dosing). Use when asked things like "stroke risk for this AF patient", "should this patient be anticoagulated", "pneumonia severity — admit or not?", "sepsis screen this patient", "DVT/PE pretest probability", "10-year cardiovascular risk", "cirrhosis severity / MELD score", or "eGFR / kidney function". Pairs CHA2DS2-VASc with HAS-BLED to weigh anticoagulation. NOT for...
What it loads into context
Frontmatter
266 tokensAlways available to the agent
SKILL.md
3,624 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Step 1 — Map the scenario to the score(s)
- Step 2 — Gather the required inputs
- Step 3 — Compute
- Step 4 — Interpret (per-score tables)
- Worked example A — Atrial fibrillation, weigh anticoagulation (paired)
- Worked example B — Cirrhosis severity and transplant priority (paired)
- Completeness checklist
- LIMITATIONS
Files
SKILL.mdcore12,525 bytesSKILL.mdcore12,525 bytesSKILL.mdcore12,525 bytesSKILL.mdcore12,525 bytes
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# Clinical Risk Scoring
Turn a clinical scenario into the right validated risk score, compute it with a deterministic calculator tool, and interpret the number into a clinical action. All 10 backing tools are pure-compute (no network, no API key) and return `{status, data:{score, interpretation, components, ...}}`.
This skill is **decision-support only** — see LIMITATIONS. It does not replace clinical judgment.
## Step 1 — Map the scenario to the score(s)
| Clinical scenario | Score(s) | Tool(s) |
|---|---|---|
| Atrial fibrillation — stroke risk / anticoagulate? | CHA2DS2-VASc **and** HAS-BLED (pair) | `ClinicalCalc_CHA2DS2_VASc` + `ClinicalCalc_HAS_BLED` |
| Community-acquired pneumonia — severity / admit? | CURB-65 | `ClinicalCalc_CURB_65` |
| Suspected sepsis (infection + ? deterioration) | qSOFA | `ClinicalCalc_qSOFA` |
| Cirrhosis / chronic liver disease severity | Child-Pugh **and** MELD-Na (pair) | `ClinicalCalc_Child_Pugh` + `ClinicalCalc_MELD_Na` |View the complete source on GitHubtooluniverse-clinical-terminology-lookup
When your agent uses it
Fast autocomplete/normalization lookups against the NLM Clinical Table Search Service — resolve a partial or informal drug name to its RxTerms display name and RxCUI, autocomplete a health condition or problem-list entry to its formal name and ICD-10-CM/ICD-9-CM codes, normalize a disease mention to a UMLS CUI, look up an HCPCS Level II billing/DME code, autocomplete a pharmacogenomic star allele (e.g. CYP2D6, CYP2C19) to its nucleotide/protein change, or find a US healthcare provider/organization by name via the NPPES NPI registry. Also covers standardized coding-system lookups: search or look up ICD-10-CM diagnosis codes directly by name or code, resolve a drug name to its full RxNorm identity (RXCUI, related brand/generic products, NDC package-level status/properties), browse NCI Thesaurus cancer-concept hierarchy and its cross-vocabulary maps to MedDRA/SNOMED/GDC, and search/resolve LOINC lab-test and clinical-form codes. Also resolves UniProt's own disease and functional-keyword...
What it loads into context
Frontmatter
256 tokensAlways available to the agent
SKILL.md
7,911 tokensWhen the skill is activated
Reference documents
8,166 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Tools
- Standardized Coding Systems: ICD-10-CM and RxNorm
- Cancer Ontology and Lab-Test Coding: NCI Thesaurus and LOINC
- Protein-Annotation Vocabularies: UniProt Disease and Keywords
- Disease Ontology and Genetic-Condition Terminology: Mondo, DO, MedGen
- Cross-Ontology Search, Text Annotation, and Specialized Terminologies
- Workflow
- Output
Files
SKILL.mdcore30,070 bytesSKILL.mdcore30,070 bytesSKILL.mdcore30,070 bytesSKILL.mdcore30,070 bytesreferences/clinical_tables_tool_reference.mddoc26,324 bytes
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# Clinical Terminology Lookup (NLM Clinical Tables, ICD, RxNorm)
A **fast, lightweight normalization utility** — not a research skill. Six
autocomplete-style tools from the NLM Clinical Table Search Service turn a
partial or informal clinical term into a standardized name/code, plus two
standardized-coding-system tool families (ICD-10-CM diagnosis codes, RxNorm
drug identity/NDC data) for when the input is already a known term/code and
you need its full standardized record rather than autocomplete matching.
Use this as a **supporting first step** before a deeper lookup in another
skill, not as a destination in itself.
**LOOK UP, DON'T GUESS**: RxCUIs, ICD-10-CM codes, UMLS CUIs, HCPCS codes,
and star-allele nucleotide changes are exact identifiers — never guess one
from memory or infer it from the term's spelling. Always resolve throughView the complete source on GitHubtooluniverse-clinical-trial-design
When your agent uses it
Strategic clinical trial design feasibility assessment. Analyzes 6 dimensions (endpoint, population, comparator, effect size, duration, regulatory pathway) using precedent trials and FDA guidance. Produces enrollment projections, endpoint recommendations, and approval-pathway analysis. Use for trial-protocol design, power/sample-size estimation, comparator selection, and FDA submission strategy. Driven by precedent-based reasoning rather than first-principles math.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
3,116 tokensWhen the skill is activated
Reference documents
30,417 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- Core Principles
- When to Use This Skill
- Core Strategy: 6 Research Paths
- Report Structure (14 Sections)
- Tool Reference by Research Path
- Quick Start Example
- Next Steps
- Integration with Other Skills
- Programmatic Access (Beyond Tools)
- Reference Files
- Version Information
Scripts executed separately
python_implementation.pytrial_pipeline.py
Files
.env.templateasset499 bytesBEST_PRACTICES.mddoc4,866 bytesEXAMPLES.mddoc59,702 bytesQUICK_START.mddoc7,732 bytesREPORT_TEMPLATE.mddoc17,798 bytesSKILL.mdcore13,410 bytesSKILL.mdcore13,410 bytesSKILL.mdcore13,410 bytesSTUDY_DESIGN_PROCEDURES.mddoc5,555 bytesWORKFLOW_DETAILS.mddoc10,204 bytespython_implementation.pyruns14,716 bytestrial_pipeline.pyruns14,716 bytes
Read SKILL.md
# Clinical Trial Design Feasibility Assessment
Systematically assess clinical trial feasibility by analyzing 6 research dimensions. Produces comprehensive feasibility reports with quantitative enrollment projections, endpoint recommendations, and regulatory pathway analysis.
**IMPORTANT**: Always use English terms in tool calls (drug names, disease names, biomarker names), even if the user writes in another language. Only try original-language terms as a fallback if English returns no results. Respond in the user's language.
## Reasoning Before Searching
Trial design starts with the question, not the methods. Answer these four questions before running any tools — they determine everything else:
1. **What is the primary endpoint?** Is it overall survival (gold standard but slow), PFS (faster but surrogate), ORR (single-arm friendly but not always accepted), or a biomarker (needs validation as surrogate first)? The endpoint determines FDA pathway, statistical design, and duration.
2. **Who is the population?** Broad unselected vs. biomarker-enriched. Enriched populations have higher response rates, allowing smaller trials — but require a validated companion diagnostic and reduce the eligible patient pool.
3. **What is the comparator?** Placebo (only if no standard of care exists), active control (requires non-inferiority or superiority framing), or single-arm with historical control (acceptable for rare diseases or breakthrough designations, but FDA scrutiny is high).
4. **Is the effect size realistic given the mechanism?** A 20% improvement in ORR over SOC requires ~100 patients per arm. A 50% improvement requires ~30. If the mechanism only justifies a 10% improvement, the trial may be underpowered regardless of design. Check precedent effect sizes in similar trials before committing to an endpoint.View the complete source on GitHubtooluniverse-clinical-trial-matching
When your agent uses it
AI-driven patient-to-trial matching for precision oncology and rare-disease care. Transforms a patient's molecular profile (mutations, biomarkers, expression) and clinical state into ranked clinical-trial recommendations with evidence tiers. Searches ClinicalTrials.gov, the EU CTIS register (European/EEA trials), AND the ISRCTN registry (UK/international) plus cross-references CIViC, OpenTargets, ChEMBL, and FDA labels. Use for matching patients to trials by genotype, biomarker-driven trial selection, trial-eligibility scoring, and finding trials across the US, Europe, and the UK.
What it loads into context
Frontmatter
141 tokensAlways available to the agent
SKILL.md
3,354 tokensWhen the skill is activated
Reference documents
13,151 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Molecular Matching Priority
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parsing
- Workflow Overview
- Critical Tool Parameters
- Scoring Summary
- Parallelization Strategy
- Error Handling
- Reference Files
Files
EXAMPLES.mddoc5,399 bytesMATCHING_ALGORITHMS.mddoc11,117 bytesQUICK_START.mddoc4,786 bytesREPORT_TEMPLATE.mddoc5,896 bytesSCORING_CRITERIA.mddoc4,615 bytesSKILL.mdcore12,934 bytesSKILL.mdcore12,934 bytesTOOLS_REFERENCE.mddoc7,457 bytesTRIAL_SEARCH_PATTERNS.mddoc11,387 bytes
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# Clinical Trial Matching for Precision Medicine
Transform patient molecular profiles and clinical characteristics into prioritized clinical trial recommendations. Searches ClinicalTrials.gov and cross-references with molecular databases (CIViC, OpenTargets, ChEMBL, FDA) to produce evidence-graded, scored trial matches.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Patient-centric** - Every recommendation considers the individual patient's profile
3. **Molecular-first matching** - Prioritize trials targeting patient's specific biomarkers
## Molecular Matching Priority
Match patients to trials by molecular profile FIRST (specific mutations), then by disease stage, then by prior treatments. A patient with EGFR L858R should match to EGFR-targeted trials regardless of other factors.
4. **Evidence-graded** - Every recommendation has an evidence tier (T1-T4)
5. **Quantitative scoring** - Trial Match Score (0-100) for every trialView the complete source on GitHubtooluniverse-clinical-trial-protocol
When your agent uses it
Generate a full clinical trial protocol document (ICH E6(R2)/FDA-style) for a drug or medical device, including background, objectives/endpoints, study design, population/eligibility, intervention details, assessment schedule, statistical analysis plan with sample-size justification, safety monitoring, and regulatory/ethical sections. Use when users say "generate a clinical trial protocol", "draft a protocol for [drug/device]", "write up the study design as a protocol document", "create an FDA submission protocol", "help me write section X of a trial protocol", or "calculate the sample size for this trial". Produces a stakeholder-ready Markdown deliverable, not a feasibility score.
What it loads into context
Frontmatter
154 tokensAlways available to the agent
SKILL.md
1,848 tokensWhen the skill is activated
Reference documents
2,473 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- NOT for (use other skills instead)
- Workflow
- Reference Files
- Integration with Other Skills
Scripts executed separately
scripts/sample_size_calculator.py
Files
SKILL.mdcore8,015 bytesSKILL.mdcore8,015 bytesreferences/protocol_sections_guide.mddoc4,641 bytesreferences/regulatory_pathway_notes.mddoc3,112 bytesscripts/sample_size_calculator.pyruns11,093 bytestemplates/protocol_template.mddoc3,115 bytes
Read SKILL.md
# Clinical Trial Protocol Generation
Turns a trial concept into an actual protocol DOCUMENT, grounded in real precedent-trial and regulatory-database lookups plus a genuine statistical sample-size calculation.
**This skill is a downstream step, not a replacement for `tooluniverse-clinical-trial-design`.** That skill answers "is this trial feasible, and what should endpoint/population/comparator/effect-size/duration/regulatory-pathway be?" (a 0-100 feasibility score with evidence grading). This skill takes those six answers — freshly run or already available from a prior `tooluniverse-clinical-trial-design` report — and drafts the actual protocol sections around them. **Do not re-derive feasibility scoring, endpoint selection reasoning, or regulatory-pathway strategy here — call `tooluniverse-clinical-trial-design` for that and consume its output.**
## When to Use This Skill
- "Write me a clinical trial protocol for [drug/device]"
- "Generate an FDA submission protocol for our Phase 2 study"
- "Draft the statistical analysis plan / sample size section"
- "I have the feasibility assessment — now turn it into a protocol document"
- "What sample size do I need for a [proportion/continuous/survival] endpoint?"
View the complete source on GitHubtooluniverse-codex-plugin
When your agent uses it
Install, set up, verify, update, pin, uninstall, or troubleshoot the ToolUniverse plugin on OpenAI Codex. ALWAYS consult this skill for any of those — don't answer from memory, because the exact marketplace name (mims-harvard/ToolUniverse), the "codex plugin marketplace add" then "codex plugin add -m tooluniverse" flow, Codex's startup auto-upgrade behavior, the uvx tooluniverse MCP server, and the API-key env vars are easy to get wrong. Use it whenever someone wants to get ToolUniverse (or "the 1000+ scientific tools" / "the harvard tools") working on Codex, says the Codex plugin or its tools/skills won't load, hits a uvx or MCP-server startup error, asks how Codex updates it, wants to pin or remove it, or finds it running an old tool version — even if they never say the word "plugin". Not for the Claude Code plugin (use tooluniverse-claude-code-plugin), for running research with the tools, or for authoring new tools or skills.
What it loads into context
Frontmatter
242 tokensAlways available to the agent
SKILL.md
2,259 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Prerequisites (check once)
- Install (two commands)
- Verify it worked
- What you get
- API keys (optional, but recommended)
- Updates
- Uninstall
- Alternative install (local checkout / air-gapped)
- Troubleshooting
- Automated diagnosis & repair
- For plugin maintainers
Scripts executed separately
../../../../scripts/release-plugin.sh
Files
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# Install the ToolUniverse Plugin for OpenAI Codex
One-step install on Codex: an MCP server exposing 1000+ scientific tools plus 130+ research skills — all auto-configured. (For the Claude Code version, see `tooluniverse-claude-code-plugin`.)
## Prerequisites (check once)
```bash
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh
codex --version # OpenAI Codex CLI; if missing: https://developers.openai.com/codex
```
## Install (two commands)
```bashView the complete source on GitHubtooluniverse-comparative-genomics
When your agent uses it
Cross-species gene comparison and ortholog analysis. Integrates Ensembl Compara orthologs, NCBI Gene, UniProt, OLS, Monarch, and OpenTargets to identify orthologs, paralogs, sequence conservation, functional conservation across species, and lineage-specific gene gains/losses. Use for phylogenetic gene tracing, model-organism mapping, and evolutionary-genomics queries.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
6,552 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Conservation Reasoning Framework
- Workflow Overview
- Phase 1: Gene Identification & Validation
- Phase 2: Ortholog Discovery
- Phase 3: Sequence Retrieval
- Phase 4: Functional Annotation Comparison
- Phase 5: Cross-Species Phenotype Bridging
- Phase 6: Gene Tree & Evolutionary Context
- Synthesis Questions
- Fallback Strategies
- Limitations
Scripts executed separately
scripts/dnds.py
Files
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# Comparative Genomics & Ortholog Analysis
Cross-species gene comparison, ortholog identification, sequence retrieval, and functional conservation analysis integrating Ensembl Compara, NCBI, UniProt, OLS, Monarch, and OpenTargets.
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
## When to Use This Skill
**Triggers**:
- "Find the mouse ortholog of [human gene]"View the complete source on GitHubtooluniverse-computational-biophysics
When your agent uses it
Solve quantitative problems in biophysics — pharmacokinetics (PK volume of distribution, clearance, half-life), epidemiology (R0, attack rate), toxicology (LD50, NOAEL), population genetics (Hardy-Weinberg, Fst), enzyme kinetics (Michaelis-Menten), thermodynamics. Use for first-principles quantitative biology calculations, dose calculations, exposure assessment, and biophysical-property estimation.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
4,536 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Recognize the Physical Process
- 2. Reasoning Patterns by Problem Type
- 3. When to Compute vs. Estimate vs. Look Up
- 4. Python Computation Templates
- 5. Multiple-Choice Strategy
- 6. Bundled Scripts
- 7. Combinatorics & Counting Problems
- 8. Common Pitfalls to Flag
Scripts executed separately
scripts/burn_fluids.pyscripts/env_risk_assessment.pyscripts/enzyme_kinetics.pyscripts/epidemiology.pyscripts/fluid_calculations.pyscripts/herd_immunity.pyscripts/iv_drip_rate.pyscripts/mc_analyzer.pyscripts/radioactive_decay.py
Files
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Read SKILL.md
# Computational Biophysics & Quantitative Biology Skill
## 1. Recognize the Physical Process
The single most important step: identify what physical process the problem describes. In quantitative biology, almost every problem maps to one of these:
- **Drug enters body → distributes → is eliminated**: pharmacokinetics. Key quantities: dose, bioavailability, volume of distribution, clearance, half-life. The body is a compartment model.
- **Radioactive tracer decays over time**: nuclear medicine. Same math as drug elimination (exponential decay) but the rate constant is a physical property of the isotope, not a patient variable.
- **Pathogen spreads through population**: epidemiology. R₀ determines whether an epidemic grows or dies. Herd immunity threshold = 1 - 1/R₀. Every epidemic model starts here.
- **Ligand binds receptor**: binding equilibrium. At low [ligand], binding is linear. At saturation, all sites occupied. Kd = concentration at half-maximal binding. This same curve describes enzyme kinetics, drug-receptor occupancy, and surface adsorption.
- **Contaminant enters environment**: dilution + persistence. Two questions: what is the concentration after mixing (conservation of mass), and how long does it persist (exponential decay with environmental half-life)?
- **Two populations differ genetically**: population genetics. Fst measures differentiation. HWE tests if mating is random. Gene flow opposes drift.
- **Neurons communicate in a network**: computational neuroscience. Integrate-and-fire models, synaptic dynamics, balanced excitation/inhibition. Mean firing rate depends on input current relative to threshold.
View the complete source on GitHubtooluniverse-connect
When your agent uses it
How to use ToolUniverse Connect (connect.aiscientist.tools), the hosted ToolUniverse service, and when to use it instead of ToolUniverse installed on this computer. Use when the user asks how to use ToolUniverse or ToolUniverse Connect, how to connect Claude, Claude Code or Codex, whether to use the hosted service or a local install, why a tool says a login or API key is missing and where to add it, how to share a model or script with colleagues, how to use a colleague's computer or a TU-SHARE code, how to publish a tool, or when ToolUniverse tools are missing, signed out or not connecting.
What it loads into context
Frontmatter
144 tokensAlways available to the agent
SKILL.md
2,030 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Find out which ToolUniverse this user has
- 2. Hosted or local: which one to suggest
- 3. Connect an AI app to ToolUniverse Connect
- 4. After connecting: what can this user use?
- 5. A tool says a login (API key) is missing or rejected
- 6. Sharing, using shared computers, publishing (Connect only)
- 7. When something goes wrong
Files
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# ToolUniverse Connect: what each part is for and how to use it
The person you are helping is usually a scientist, not a programmer. Answer in plain words, one step at a time. When you can do a step yourself (run a command, call a tool), do it; ask the user only for what only they can do, such as signing in or clicking **Allow**.
**Never ask the user to paste a key, token or password into the chat, and never write one into a file or command yourself.**
## 1. Find out which ToolUniverse this user has
| What you see | The user is on |
|---|---|
| ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which still works) | **ToolUniverse Connect** (hosted) |
| A ToolUniverse server started on this computer (`uvx tooluniverse`, `tu serve`), the `tu` command, or Python `from tooluniverse import ToolUniverse` | **Local ToolUniverse** |
| No ToolUniverse tools at all | **Not connected yet** |
View the complete source on GitHubtooluniverse-crispr-screen-analysis
When your agent uses it
Analyze CRISPR-Cas9 genetic screens — MAGeCK gene-level scores, sgRNA count QC, replicate correlation, hit prioritization, and pathway GSEA on screen output. Use for genome-wide essentiality screens, synthetic-lethality discovery, dropout vs positive-selection screen analysis, target identification, and resistance-screen interpretation. Includes screen-QC and statistical thresholds.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
2,705 tokensWhen the skill is activated
Reference documents
16,188 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- RULE ZERO — Check for pre-computed results FIRST
- Overview
- Core Workflow
- ToolUniverse Tool Integration
- Quick Start
- Domain Reasoning: Hits Are Statistical, Not Biological
- Interpretation Framework
- References
- Analysis conventions
- See Also
Scripts executed separately
scripts/reference_gene_sets.py
Files
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Read SKILL.md
# ToolUniverse CRISPR Screen Analysis
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
---
Comprehensive skill for analyzing CRISPR-Cas9 genetic screens to identify essential genes, synthetic lethal interactions, and therapeutic targets through robust statistical analysis and pathway enrichment.View the complete source on GitHubtooluniverse-custom-tool
When your agent uses it
Add custom local tools to ToolUniverse alongside the 1000+ built-in tools. Covers JSON-config tools (simplest, no code), Python class tools (REST/SOAP/GraphQL APIs, computational logic), and best-practices for return schemas. Use for wrapping new APIs, adding domain-specific computations, or contributing tools to the registry.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
2,251 tokensWhen the skill is activated
Reference documents
8,476 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Option A — Workspace tools (local use)
- Option B — Plugin package (shareable, pip-installable)
- Offline / pure-computation tools
Files
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# Adding Custom Tools to ToolUniverse
**When to create a custom tool:** Create one if you need to access an API that ToolUniverse
doesn't cover, or if you need a specialized data transformation that no existing tool provides.
Start with the JSON config approach (simplest — no Python needed); escalate to a Python class
only if you need custom response parsing or stateful logic.
Three ways to add tools — pick the one that fits your needs:
| Approach | When to use |
|---|---|
| **JSON config** | REST API with standard request/response — no coding needed |
| **Python class (workspace)** | Custom logic for local/private use only |
| **Plugin package** | Reusable tools you want to share or install via pip |View the complete source on GitHubtooluniverse-data-integration-analysis
When your agent uses it
Integrate computed statistical results (DEGs, GWAS hits, associations) with biological context from ToolUniverse databases (UniProt, GO, Reactome, ClinVar, OpenTargets). Use for adding gene function/pathway/disease annotations to a result list, building biological narrative around statistical findings, and going beyond p-values to mechanism.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
1,802 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Step 1: Statistical Results to Biological Questions
- Step 2: Multi-Database Evidence Integration
- Step 3: Causal Reasoning
- Step 4: Cross-Validation
- Step 5: Actionable Reporting
Files
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do -- execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Data Integration Analysis
Bridge the gap between statistical results and biological understanding. After any computational analysis produces significant findings, this skill teaches how to interpret them using ToolUniverse's biological knowledge tools -- the key advantage over platforms that only do data analysis.
**IMPORTANT**: Always use English terms in tool calls (gene names, pathway names, organism names), even if the user writes in another language. Respond in the user's language.
---
## When to Use This Skill
Apply when:View the complete source on GitHubtooluniverse-data-wrangling
When your agent uses it
Universal data access patterns for downloading and parsing scientific data when ToolUniverse tools don't cover the source, only return metadata, or you need bulk records. Use for VCF/h5ad/BAM/SDF/GCT parsing, multi-step API workflows (search to filter to download to parse), thousands of records at once, or sources with no dedicated tool. Write Python code via Bash for every step.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
4,619 tokensWhen the skill is activated
Reference documents
3,341 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Decision: Tool vs Code
- Section A: Format Cookbook
- Section B: API Patterns by Domain
- Section C: Restricted/Uncovered Data Sources
- Section D: Universal Patterns
Files
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# Data Wrangling: Universal Access Patterns
Reference for downloading and parsing scientific data from any source. Write and run Python code via Bash for every step.
## When to Use
- ToolUniverse tool returned metadata/search results but you need **raw or bulk data**
- Data is in a format tools don't parse (VCF, h5ad, BAM, SDF, GCT)
- You need a **multi-step API workflow** (search -> filter -> download -> parse)
- The data source has **no ToolUniverse tool** at all
- You need **thousands of records**, not the 10-100 a tool returns
## Decision: Tool vs Code
View the complete source on GitHubtooluniverse-database-logins
When your agent uses it
Help the user add the login (API key) that a ToolUniverse tool needs for a database such as NCBI/PubMed, OMIM, DisGeNET, UMLS or OpenAlex, without the key ever passing through the chat. Explains which login unlocks what, where to get it, where to paste it, and retries the tool. Use when a tool says a login, API key or token is missing or rejected, or the user asks "which logins do I need", "add my OMIM key", or "why does this database need a key".
What it loads into context
Frontmatter
134 tokensAlways available to the agent
SKILL.md
1,063 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Find out which login is needed
- 2. Get the key (the user does this)
- 3. Save it where ToolUniverse can use it
- 4. Check it works
Files
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# Add a database login for ToolUniverse
Most ToolUniverse tools need no login. A few databases require the user's own free or licensed account. The person you are helping is usually a scientist, not a programmer: explain in plain words, one step at a time.
**Never ask the user to paste a key, token or password into the chat, and never put one in a file, command or message yourself.** Keys are typed only into the ToolUniverse website (hosted use) or the user's own local settings (local use).
## 1. Find out which login is needed
- If a tool failed, its error names the missing or rejected setting (for example `OMIM_API_KEY`). Use that exact name.
- If the user asks in general, ask what they want to do, and suggest only the logins that unlock it. Do not push them to collect every key.
Common ones:
| Login | Unlocks | Where to get it | Cost / wait |View the complete source on GitHubtooluniverse-dataset-discovery
When your agent uses it
Find and evaluate research datasets for any scientific question. Maps research questions to required study designs (longitudinal vs cross-sectional, observational vs experimental, single-cohort vs multi-cohort). Use when the user asks 'find data about X', 'where can I get data on Y', or needs a specific cohort/survey/repository. Covers GEO, ArrayExpress, dbGaP, NHANES, UK Biobank, ClinicalTrials.gov, GWAS Catalog, and 30+ scientific repositories.
What it loads into context
Frontmatter
114 tokensAlways available to the agent
SKILL.md
2,398 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Step 1: Understand What the Research Question Requires
- Step 2: Search Strategy
- Step 3: Evaluate Dataset Fitness
- Step 4: Download and Analyze
- Step 5: Report Honestly
- LOOK UP, DON'T GUESS
Files
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# Dataset Discovery
## When to Use
- User asks "find me data about X" or "where can I get data on Y"
- User wants to analyze a relationship between variables
- User needs specific study designs (longitudinal, cross-sectional, experimental)
- User asks about specific surveys or cohorts
## Step 1: Understand What the Research Question Requires
Before searching, determine the **minimum data requirements**:
**Study design needed:**
- "Does X predict CHANGES in Y over time?" → longitudinal (same people measured repeatedly). Cross-sectional data CANNOT answer this — don't settle for it.View the complete source on GitHubtooluniverse-diagnostic-test-evaluation
When your agent uses it
Diagnostic test / biomarker accuracy — sensitivity, specificity, PPV, NPV, likelihood ratios, accuracy from a 2x2 table; ROC curve, AUC, and the optimal cutoff (Youden) for a continuous biomarker; and post-test probability via Bayes. Use when you have test results vs a gold standard (binary 2x2, or a continuous score + true labels) and need to judge how good the test is, pick a threshold, or compute the probability of disease given a result. Emphasizes the prevalence-dependence of PPV/NPV.
What it loads into context
Frontmatter
136 tokensAlways available to the agent
SKILL.md
1,460 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Which case are you in?
- Step 1 — Fixed-cutoff metrics from a 2×2 table
- Step 2 — ROC / AUC / optimal cutoff for a continuous biomarker
- Step 3 — Post-test probability (Bayes)
- Gotchas (state these)
- Honest limitations
- Related skills
Scripts executed separately
scripts/roc_analysis.py
Files
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# Diagnostic Test / Biomarker Accuracy Evaluation
Judge how well a test or biomarker discriminates disease — at a fixed cutoff (2×2) or across all cutoffs (ROC) — and turn a result into a probability of disease.
## Which case are you in?
| You have… | Go to |
|---|---|
| A 2×2 table (TP/FP/TN/FN) at a fixed cutoff | **Step 1** (`Epidemiology_diagnostic`) |
| A **continuous** biomarker score + true labels | **Step 2** (ROC / AUC / Youden, Python) |
| A test's sens/spec + a patient's pre-test probability | **Step 3** (`Epidemiology_bayesian`) |
## Step 1 — Fixed-cutoff metrics from a 2×2 table
View the complete source on GitHubtooluniverse-disease-research
When your agent uses it
Generate comprehensive disease research reports covering genetics (causal genes, GWAS, OMIM), pathways (Reactome, KEGG), drugs (existing therapies, repurposing candidates), clinical trials, epidemiology (prevalence, incidence), and phenotypes (HPO). Use for full disease overviews, comprehensive disease characterization, and orphan/rare-disease profiling.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
2,485 tokensWhen the skill is activated
Reference documents
16,809 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- When to Use
- Core Workflow: Report-First Approach
- Disease Mechanism Reasoning
- 10 Research Dimensions
- Report Template
- Citation Format
- Progressive Update Pattern
- Evidence Grading & Interpretation
- Final Report Quality Checklist
- Expected Output Scale
- Cross-Skill References
- Reference Files
Scripts executed separately
../../../../skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py
Files
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# ToolUniverse Disease Research
Generate a comprehensive disease research report with full source citations. The report is created as a markdown file and progressively updated during research.
**IMPORTANT**: Always use English disease names and search terms in tool calls. Respond in the user's language.
---
## LOOK UP, DON'T GUESS
When asked about a disease, query Orphanet/OMIM/DisGeNET FIRST. Don't rely on memory for prevalence, genetics, or treatment — these change over time. When you're not sure about a fact, your first instinct should be to SEARCH for it using tools, not to reason harder from memory.
---
View the complete source on GitHubtooluniverse-dose-response
When your agent uses it
Dose-response / concentration-response curve fitting — IC50, EC50, Hill slope, Emax/Emin efficacy, and relative potency from paired concentration vs response data (enzyme/cell assays, drug screening, agonist/antagonist pharmacology). Fits the 4-parameter logistic (Hill sigmoidal) model. Use when you have concentrations + responses and need a potency value, to compare two compounds' potency, or to judge curve quality. NOT for image-derived dose-response (use tooluniverse-image-analysis) and NOT for survival/regression (use tooluniverse-statistical-modeling).
What it loads into context
Frontmatter
133 tokensAlways available to the agent
SKILL.md
1,622 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- Step 1 — Prepare the data (where results go wrong)
- Step 2 — Fit / get the potency
- Step 3 — Interpret the four parameters
- Step 4 — Quality gotchas (state these, don't hide them)
- Honest limitations
- Related skills
Scripts executed separately
scripts/fit_dose_response.py
Files
SKILL.mdcore6,231 bytesSKILL.mdcore6,231 bytesscripts/fit_dose_response.pyruns4,033 bytes
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# Dose-Response / Concentration-Response Analysis
Turn paired **concentration vs response** measurements into a potency (IC50/EC50), a Hill slope, an efficacy (Emax), and a quality judgment — and compare potency between compounds.
## When to use this
- Enzyme inhibition / activation assays, cell viability, reporter assays, radioligand binding, agonist/antagonist pharmacology.
- You have a list of concentrations and the response at each, and want IC50/EC50 + Hill slope.
- You want to say "compound A is N-fold more potent than B."
The model is the 4-parameter logistic (4PL) / Hill sigmoidal:
`f(x) = Emin + (Emax − Emin) / (1 + (EC50/x)^n)` — where `n` is the Hill slope.
## Step 1 — Prepare the data (where results go wrong)View the complete source on GitHubtooluniverse-drug-drug-interaction
When your agent uses it
Assess drug-drug interactions — CYP metabolic interactions (substrate/inhibitor/inducer), transporter (P-gp, BCRP, OATP) effects, pharmacodynamic synergy/antagonism, clinical significance scoring, and management recommendations. Use for polypharmacy review, prescribing decision support, and safety analysis when adding or switching drugs.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
4,706 tokensWhen the skill is activated
Reference documents
3,823 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOCAL PHARMACOLOGY REFERENCE (USE FIRST)
- LOOK UP, DON'T GUESS
- New Symptom After New Medication: First-Line Reasoning
- When to Use This Skill
- Clinical Reasoning Framework
- Critical Workflow Requirements
- Tool Workflow
- Output Report Structure
- Success Criteria
Scripts executed separately
ddi_pipeline.pyddi_working_example.pypython_implementation.pyscripts/pharmacology_ref.py
Files
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# Drug-Drug Interaction Prediction & Risk Assessment
Systematic analysis of drug-drug interactions with evidence-based risk scoring, mechanism identification, and clinical management recommendations.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create DDI_risk_report.md FIRST, then populate progressively
2. **Bidirectional analysis** - Always analyze A→B and B→A interactions (effects may differ)
3. **Evidence grading** - Grade all DDI claims by evidence quality (★★★ FDA label, ★★☆ clinical study, ★☆☆ theoretical)
4. **Risk scoring** - Multi-dimensional scoring (0-100) combining mechanism + severity + clinical evidence
5. **Patient safety focus** - Provide actionable clinical guidance, not just theoretical interactions
6. **Mandatory completeness** - All analysis sections must exist with explicit "No interaction found" when appropriate
---
View the complete source on GitHubtooluniverse-drug-mechanism-research
When your agent uses it
Trace drug mechanism of action — primary target → downstream signaling → pathway perturbation → tissue/organ effect → clinical outcome. Uses DrugBank, ChEMBL, KEGG, Reactome, STRING. Use for understanding how a drug works, identifying off-target effects, mechanism-based combination therapy design, and writing mechanism sections of reports.
What it loads into context
Frontmatter
170 tokensAlways available to the agent
SKILL.md
3,069 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Investigation Philosophy
- When to Use
- NOT for (use other skills)
- Step 1: Resolve the Drug
- Step 2: Identify the Primary Target
- Step 3: Assess Off-Target Effects
- Step 4: Map to Pathway Context
- Step 5: Get the Regulatory View (DailyMed)
- Step 6: Check Pharmacogenomics
- Step 7: Gather Literature Evidence
- Step 8: Integrate and Report
- Comparing Two Drugs
- Fallback Strategies
Files
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# Drug Mechanism of Action Investigation
## Investigation Philosophy
Drug mechanism research follows one core question chain:
**Target -> Downstream Effect -> Pathway -> Organ Effect -> Clinical Outcome**
Start with the drug's primary target. What receptor, enzyme, or transporter does it bind? Then trace forward: what does inhibiting/activating that target do immediately? What pathway is disrupted? What organ-level change results? What does the patient experience?
The LLM already knows drug pharmacology. This skill teaches HOW TO INVESTIGATE using available tools, not what mechanisms exist.
## When to Use
View the complete source on GitHubtooluniverse-drug-regulatory
When your agent uses it
Drug regulatory and approval research — FDA substance registry, ATC/EPC classification, EMA decisions, generic-drug status, FDA Orange Book exclusivity, NDA/BLA pathways. Use for jurisdiction-aware approval status (FDA vs EMA), generic vs brand availability, exclusivity expiry tracking, and regulatory pathway selection. Always specifies the market when reporting status.
What it loads into context
Frontmatter
177 tokensAlways available to the agent
SKILL.md
3,862 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- NOT for (use other skills instead)
- Workflow Overview
- Phase 1: Substance Identification (FDAGSRS)
- Phase 2: Drug Classification (RxClass)
- Phase 3: Approval & Generic Status (FDA Orange Book / Purple Book)
- Phase 4: Patent & Exclusivity
- Phase 5: Label Parsing (DailyMed)
- Phase 6: Clinical Trials
- Example Workflows
- Common Mistakes
- Reasoning Framework
Files
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# Drug Regulatory Research
**Regulatory status depends on jurisdiction.** FDA approval does not equal EMA approval — check the specific market the user is asking about. Generic availability depends on BOTH patent expiry AND regulatory approval — a patent may have expired but no ANDA may yet be filed or approved. Exclusivity codes (NCE, ODE, PED) can block generics even after patent expiry; always check `FDA_OrangeBook_get_exclusivity` before concluding a generic can enter. A 505(b)(2) NDA is not a generic — it requires its own clinical data and gets its own exclusivity period.
**LOOK UP DON'T GUESS**: never assume NDA numbers, exclusivity dates, or ATC codes — always call FDAGSRS, Orange Book, and RxClass tools to retrieve current data; regulatory status changes with new approvals and expirations.
Regulatory intelligence for drugs: identify FDA substances, classify drugs by therapeutic
category, check approval and generic status, retrieve label sections, and find clinical trials.
## When to Use
- "What is the FDA regulatory status of semaglutide?"
- "Is there a generic for Humira?"
- "What ATC class does metformin belong to?"View the complete source on GitHubtooluniverse-drug-repurposing
When your agent uses it
Identify drug repurposing candidates via target-based, compound-based, and disease-based strategies. Combines drug-target-disease network reasoning with mechanism rationale, clinical-trial precedent, and patent/regulatory feasibility. Use for hypothesis-generating repurposing for orphan diseases, finding existing drugs for new indications, and prioritizing candidates by evidence and feasibility.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
4,624 tokensWhen the skill is activated
Reference documents
20,568 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- Core Strategies
- Workflow Overview
- Quick Start
- Key ToolUniverse Tools
- Scoring & Decision Framework
- Best Practices
- Troubleshooting
- Reference Files
Files
EXAMPLES.mddoc36,905 bytesPROCEDURES.mddoc11,799 bytesREFERENCE.mddoc20,400 bytesREPORT_TEMPLATE.mddoc1,772 bytesSKILL.mdcore19,811 bytesSKILL.mdcore19,811 bytesSKILL.mdcore19,811 bytesstrategies_and_patterns.mddoc8,691 bytes
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# Drug Repurposing with ToolUniverse
Systematically identify and evaluate drug repurposing candidates using multiple computational strategies.
**IMPORTANT**: Always use English terms in tool calls. Respond in the user's language.
---
## Reasoning Before Searching
Start by asking: WHY might this drug work for a new disease? Three strategies:
- **(a) Same target**: The drug's primary target is also involved in the new disease. This is the strongest hypothesis — use OpenTargets to check if the target has genetic evidence in both diseases before any other search.
- **(b) Off-target activity**: The drug has secondary targets or off-target effects that are relevant to the new disease. Check ChEMBL bioactivity data for all known targets of the drug, not just its primary one.View the complete source on GitHubtooluniverse-drug-research
When your agent uses it
Comprehensive drug profiling — mechanism, primary/secondary targets, drug interactions, clinical-trial status, adverse events (FAERS), pharmacogenomics, and approval history. Use for full drug investigation reports, 'tell me about drug X' queries, and assembling drug profiles for clinicians, researchers, or regulatory work.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
3,405 tokensWhen the skill is activated
Reference documents
17,645 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- Drug Mechanism Reasoning
- Workflow Overview
- Compound Disambiguation (Phase 1)
- Research Paths Summary
- FDA Label Core Fields
- Fallback Chains
- Quick Reference: Tools by Use Case
- Type Normalization
- Common Use Cases
- When NOT to Use This Skill
- Cross-Skill References
- Additional Resources
Scripts executed separately
../../../../skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py
Files
CHECKLIST.mddoc8,067 bytesEXAMPLES.mddoc17,597 bytesREPORT_GUIDELINES.mddoc10,855 bytesREPORT_TEMPLATE.mddoc6,009 bytesSKILL.mdcore13,198 bytesSKILL.mdcore13,198 bytesSKILL.mdcore13,198 bytesTOOLS_REFERENCE.mddoc20,694 bytes../../../../skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.pyruns36,294 bytes
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# Drug Research Strategy
Comprehensive drug investigation using 50+ ToolUniverse tools across chemical databases, clinical trials, adverse events, pharmacogenomics, and literature.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Compound disambiguation FIRST** - Resolve identifiers before research
3. **Citation requirements** - Every fact must have inline source attribution
4. **Evidence grading** - Grade claims by evidence strength (T1-T4)
5. **Mandatory completeness** - All sections must exist, even if "data unavailable"
6. **English-first queries** - Always use English drug/compound names in tool calls, even if the user writes in another language. Only try original-language terms as a fallback. Respond in the user's language
---
View the complete source on GitHubtooluniverse-drug-synergy
When your agent uses it
Drug-combination synergy analysis — quantify whether two drugs together are synergistic, additive, or antagonistic using the standard reference models (Bliss independence, HSA / highest single agent, Loewe additivity, ZIP, and the Chou-Talalay Combination Index). Use when you have measured single-drug and combination effects (inhibition/viability) and need a synergy score. Explains which model to use, what data each one needs, and how to read the score. NOT for looking up pre-computed synergy in a database (use the SYNERGxDB tool / cell-line-profiling skill).
What it loads into context
Frontmatter
141 tokensAlways available to the agent
SKILL.md
1,507 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Step 0 — Pick the model by the data you have
- Step 1 — What each model's "additivity" means
- Step 2 — Run it
- Step 3 — Interpret the score
- Step 4 — Gotchas (state these)
- Honest limitations
- Related skills
Scripts executed separately
scripts/synergy_reference.py
Files
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# Drug-Combination Synergy Analysis
Decide whether a two-drug combination does **more than expected** (synergy), exactly as expected (additivity), or **less** (antagonism) — and pick the right reference model for the data you have.
"Synergy" only means something relative to a **null model of additivity**, and the models define additivity differently — so the first decision is *which model*, driven by *what data you measured*.
## Step 0 — Pick the model by the data you have
| You measured… | Use model | Tool | Input |
|---|---|---|---|
| Single effects of A, B, and A+B at **one** dose pair | **Bliss** | `DrugSynergy_calculate_bliss` | `effect_a`, `effect_b`, `effect_combination` (each a fraction 0–1) |
| Effects of A, B, A+B across **several** dose points | **HSA** | `DrugSynergy_calculate_hsa` | `effects_a`, `effects_b`, `effects_combo` (arrays) |
| Single-agent **dose-response curves** + one combination point | **Loewe** | `DrugSynergy_calculate_loewe` | `doses_a_single`/`effects_a_single`, `doses_b_single`/`effects_b_single`, `dose_a_combo`, `dose_b_combo`, `effect_combo` |
| Single-agent dose-response + combo point, want Chou-Talalay CI | **Combination Index** | `DrugSynergy_calculate_ci` | same as Loewe + `assumption` |View the complete source on GitHubtooluniverse-drug-target-validation
When your agent uses it
Quantitative drug-target validation pipeline. Scores druggability, selectivity, safety profile, ADMET feasibility, and structural tractability with a composite Target Validation Score (0-100) and GO/NO-GO recommendation. Use for go/no-go decisions on a target before commit-to-medchem, target prioritization across a list, and target-deselection rationale.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
4,877 tokensWhen the skill is activated
Reference documents
8,191 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- COMPUTE, DON'T DESCRIBE
- Key Principles
- When to Use
- Input Parameters
- Reference Files
- Scoring Overview
- Pipeline Phases
- Report Output
Files
QUICK_START.mddoc3,984 bytesREPORT_TEMPLATE.mddoc7,662 bytesSCORING_CRITERIA.mddoc6,481 bytesSKILL.mdcore19,387 bytesSKILL.mdcore19,387 bytesTOOL_REFERENCE.mddoc11,113 bytes
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# Drug Target Validation Pipeline
Validate drug target hypotheses using multi-dimensional computational evidence before committing to wet-lab work. Produces a quantitative Target Validation Score (0-100) with priority tier classification and GO/NO-GO recommendation.
## Reasoning Before Searching
A valid drug target must pass 4 gates in order. Failing an early gate makes later gates irrelevant:
1. **Genetic evidence linking it to disease**: Does human genetic data (GWAS, rare variant studies, Mendelian genetics) support this target's role? Genetic evidence is the strongest predictor of clinical success. Use OpenTargets and GWAS catalog before anything else. If no genetic link exists, the hypothesis is speculative — document this clearly.
2. **Druggability**: Can a molecule reach and modulate the target? Check structure availability (PDB, AlphaFold), binding pocket prediction (ProteinsPlus), target class (kinase, GPCR, nuclear receptor = favorable; transcription factor, scaffold protein = difficult), and existing chemical probes.
3. **Safety — essentiality in normal tissue**: Is the target expressed in critical tissues (heart, liver, bone marrow)? Is knockout lethal in mice? High expression in essential tissue or lethality in mouse models is a strong safety red flag even before any clinical data.
4. **Competitive landscape**: Are other drugs already approved or in late-stage trials for this target? If so, the bar is differentiation, not first-in-class. Check ChEMBL, DrugBank, and ClinicalTrials.gov early.
Do not proceed to Phase 3 (Chemical Matter) before completing Phase 1 (Disease Association). Gate 1 failures should prompt a NO-GO or pivot recommendation.View the complete source on GitHubtooluniverse-ecology-biodiversity
When your agent uses it
Ecology, biodiversity, and conservation biology research — species identification (GBIF, NCBI Taxonomy), invasive species impact, ecosystem dynamics, conservation status (IUCN), niche ecology. Use for biodiversity questions, species comparison, invasion biology, conservation prioritization, and ecology-related literature search.
What it loads into context
Frontmatter
76 tokensAlways available to the agent
SKILL.md
3,073 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- Available Tools
- Navigating the GBIF taxonomic tree
- Additional Taxonomy & Biodiversity Sources
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
Files
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# Ecology & Biodiversity Research
## Reasoning Strategy
### 1. Species & Taxonomy Questions
When a question involves identifying or comparing species:
1. **LOOK UP DON'T GUESS** — Use `GBIF_search_species` to get taxonomy, `WoRMS_search_species` for marine organisms
2. If the question asks about invasive species impacts, consider: ecological niche overlap, reproductive rate, predator release, and ecosystem engineering effects
3. Use `PubMed_search_articles` or `EuropePMC_search_articles` to find studies on specific ecological impacts
### 2. Invasive Species Impact Assessment
**Reasoning framework** — when comparing invasive species impacts:
1. **Identify the ecosystem**: What habitat/biome is affected?
2. **Assess impact mechanisms**: Competition? Predation? Disease vector? Habitat modification? Hybridization?View the complete source on GitHubtooluniverse-electron-microscopy
When your agent uses it
Search and analyze electron microscopy data — cryo-EM density maps (EMDB), fitted atomic models (PDB), raw micrograph datasets (EMPIAR), and cryo-electron tomography volumes (CryoET Data Portal). Use for finding 3D structural data on a protein/complex, comparing experimental EM resolution to AlphaFold confidence, and accessing raw EM data for re-processing.
What it loads into context
Frontmatter
94 tokensAlways available to the agent
SKILL.md
3,824 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Databases
- Workflow Overview
- Phase Details
- Common Analysis Patterns
- Edge Cases & Fallbacks
- Related Resource: IDR (light microscopy & high-content screening)
- Limitations
Files
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# Electron Microscopy Structure Analysis
Pipeline for discovering and analyzing electron microscopy data across the full resolution spectrum: from 3D density maps (EMDB) to fitted atomic models (PDB), raw micrograph datasets (EMPIAR), and cryo-electron tomography volumes (CryoET Data Portal). Connects EM data to structural biology context via PDB and AlphaFold.
**Guiding principles**:
1. **Resolution awareness** -- always report and interpret map resolution; sub-4A enables atomic modeling, 4-8A enables domain fitting, >8A is shape-level
2. **Map before model** -- the density map is the primary experimental data; fitted models are interpretations
3. **Method matters** -- single particle analysis, tomography, 2D crystallography, and helical reconstruction have different strengths and limitations
4. **Raw data value** -- EMPIAR raw data enables reprocessing with newer algorithms; always note availability
5. **Cross-reference structures** -- connect EMDB maps to PDB entries and AlphaFold predictions for completeness
6. **English-first queries** -- use English terms in tool calls
EM resolution determines what you can see. TEM resolves individual protein complexes (~2nm). Cryo-EM achieves near-atomic resolution (<4Å) for large complexes. SEM shows surface topology. Choose the right EM modality for the question.
View the complete source on GitHubtooluniverse-enzyme-design
When your agent uses it
De novo enzyme design by catalytic-motif scaffolding — build a transition-state motif, generate scaffolds with AlphaProtein Novo, fold every mechanistic state, and triage with mechanism-inspired filters (catalytic geometry, multi-state H-bonds, multi-resequence backbone scoring). Use for designing enzymes for a named reaction, scoring or filtering existing designs, or deciding which designs to order.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,154 tokensWhen the skill is activated
Reference documents
4,967 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- What AP Novo is, exactly
- Workflow Overview
- Phase 1 — Define the reaction and build the motif
- Phase 2 — Validate the motif spec first
- Phase 3 — Generate scaffolds
- Phase 4 — Resequence, or don't
- Phase 5 — Fold every mechanistic state
- Phase 6 — Evaluate and filter
- Phase 7 — Backbone-ensemble scoring (MR / MPD)
- Phase 8 — Triage, novelty check, report
- Reference files
Files
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# De Novo Enzyme Designer
Design enzymes for a named reaction by scaffolding a catalytic motif, then triage the output with filters derived from the reaction's mechanism. Built around AlphaProtein Novo (AP Novo), whose own finding is that **the filters, not the generator, decided success**.
**KEY PRINCIPLES**:
1. **Motif-first** — the input motif dominates the outcome more than any model choice
2. **Mechanism-inspired metrics** — catalytic geometry beats generic confidence
3. **Multi-state** — fold every state along the mechanism, require agreement across them
4. **Backbone quality is an ensemble property** — score a backbone by how many resequences of it pass
5. **Report the funnel, not the survivors** — know which filter killed what
6. **In-silico pass is not activity** — grade designs, never promise them
7. **English-first queries** — always use English terms in tool calls
De novo enzyme design starts with chemistry, not protein. You must be able to draw the transition state or a covalent intermediate as 3D coordinates — catalytic side-chain atoms plus ligand — before any model is useful. Everything downstream is scaffolding that geometry and then asking whether a predictor still believes it.View the complete source on GitHubtooluniverse-enzyme-kinetics
When your agent uses it
Enzyme kinetics — Michaelis-Menten Km, Vmax, kcat (turnover), and kcat/Km (catalytic efficiency / specificity constant) from substrate-velocity data, plus inhibition-mechanism analysis (competitive / uncompetitive / non-competitive, Ki). Fits the MM equation by nonlinear regression (and reports Lineweaver-Burk for reference). Use when you have substrate concentrations and initial reaction velocities and need kinetic parameters or to classify an inhibitor. NOT for BRENDA database lookups of published constants (use the BRENDA tools).
What it loads into context
Frontmatter
129 tokensAlways available to the agent
SKILL.md
1,864 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- Step 1 — Prepare the data
- Step 2 — Fit Michaelis-Menten
- Step 3 — Interpret
- Step 4 — Inhibition mechanism
- Step 5 — Gotchas (state these)
- Honest limitations
- Related skills
Scripts executed separately
scripts/fit_michaelis_menten.py
Files
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# Enzyme Kinetics (Michaelis-Menten)
Turn **substrate concentration vs initial velocity** data into Km, Vmax, kcat, and catalytic efficiency — and classify an inhibitor's mechanism.
The Michaelis-Menten model: `v = Vmax·[S] / (Km + [S])`.
## When to use this
- You measured initial reaction rates at several substrate concentrations.
- You need Km (substrate affinity), Vmax, kcat (turnover number), or kcat/Km.
- You have ±inhibitor velocity data and want to classify the inhibition mode + Ki.
For *published* kinetic constants (someone else's Km/kcat), use the BRENDA tools or `SABIO_RK_search_reactions` instead — this skill is for analyzing **your own measured** data.
View the complete source on GitHubtooluniverse-epidemiological-analysis
When your agent uses it
End-to-end observational epidemiology analysis — from research question (PECO Population/Exposure/Comparator/Outcome) to publication-ready statistical report. Covers cohort/case-control/cross-sectional design, regression with confounders, propensity scoring, sensitivity analysis. Writes Python code for every step. Use for epidemiology study analysis, NHANES/UK-Biobank-style analyses.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
4,820 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Step 1: Formulate the Research Question (PECO Framework)
- Step 2: Find and Evaluate Data
- Step 3: Download and Prepare Data
- Step 4: Descriptive Statistics (Table 1)
- Step 5: Regression Analysis
- Step 6: Sensitivity Analyses
- Step 7: Biological Interpretation (ToolUniverse Advantage)
- Step 8: Visualization
- Step 9: Report Structure
- Completeness Checklist
Files
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# Epidemiological Data Analysis
Complete workflow for observational epidemiology — from research question to publication-ready report. Write and run Python code for every step. Never describe what you "would do" — do it.
## Step 1: Formulate the Research Question (PECO Framework)
Define **P**opulation, **E**xposure, **C**omparator, **O**utcome before touching data.
- **Population**: Who? (e.g., adults aged 20-79, cancer patients stage III+, ICU admissions)
- **Exposure**: What factor? (e.g., nutrient intake, drug treatment, gene mutation, environmental pollutant)
- **Comparator**: Vs. what? (e.g., lowest tertile, unexposed, wild-type, placebo)
- **Outcome**: What health event? (e.g., disease incidence, survival time, biomarker level, mortality)
**Study design check**: Does the question require temporality?View the complete source on GitHubtooluniverse-epigenomics
When your agent uses it
Genomics and epigenomics analysis: DNA methylation (CpG, 5mC, 5hmC, bisulfite, RRBS), m6A RNA modification (MeRIP-seq), ChIP-seq peaks, ATAC-seq accessibility, histone modifications, chromatin state, multi-omics integration. Combines pandas/scipy/pysam computation with ToolUniverse annotation tools. Use for genome-wide epigenomic statistics, methylation analysis, and chromatin-genome integration.
What it loads into context
Frontmatter
118 tokensAlways available to the agent
SKILL.md
3,102 tokensWhen the skill is activated
Reference documents
13,495 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- ⚠<U+FE0F> TOP-OF-MIND RULE: long-format methylation CSV — count ROWS, not unique positions
- RULE ZERO — Check for pre-computed results FIRST
- LOOK UP, DON'T GUESS
- When to Use
- Key Principles
- PRIMARY SCRIPT — methylation_density.py (use FIRST for CpG-density questions)
- Distinguish "rows" vs "unique sites" — methylation CSVs are usually long-format
- Workflow
- Common Patterns
- GTEx Tissue IDs
- Evidence Grading
- Limitations
- Reference Files
Scripts executed separately
scripts/methylation_density.py
Files
.env.templateasset499 bytesANALYSIS_PROCEDURES.mddoc5,078 bytesCODE_REFERENCE.mddoc33,821 bytesQUICK_START.mddoc8,934 bytesSKILL.mdcore12,176 bytesSKILL.mdcore12,176 bytesSKILL.mdcore12,176 bytesTOOLS_REFERENCE.mddoc3,733 bytesscripts/methylation_density.pyruns8,801 bytes
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# Genomics and Epigenomics Data Processing
## ⚠<U+FE0F> TOP-OF-MIND RULE: long-format methylation CSV — count ROWS, not unique positions
When the input is a long-format methylation CSV (one row per `(sample, CpG_position)`
e.g. columns `Pos, Chromosome, MethylationPercentage`), "how many sites are
removed when filtering" almost always means **rows removed**, NOT unique-position
removals. The two answers differ by a factor of ≈ `n_samples`.
| Question phrasing | What it means |
|---|---|
| "how many sites are removed when filtering …" | **rows removed** (= samples × positions failing the filter) |
| "how many unique CpG sites pass filter" | **unique positions** (dedupe by `Pos` then filter) |
View the complete source on GitHubtooluniverse-epigenomics-chromatin
When your agent uses it
Histone-modification ChIP-seq, ATAC-seq accessibility, chromatin state, and TF binding analysis from ENCODE, Roadmap Epigenomics, ChIP-Atlas. Use for chromatin-state-by-tissue queries, TF-binding-by-region, regulatory landscape mapping, and ENCODE-cCRE annotations. For DNA methylation use tooluniverse-epigenomics; for RNA-seq use tooluniverse-rnaseq-deseq2.
What it loads into context
Frontmatter
221 tokensAlways available to the agent
SKILL.md
3,715 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- NOT for (use other skills instead)
- Reasoning: Classify the Question First
- Reasoning: Histone Marks
- Reasoning: eQTL Interpretation
- Reasoning: Variant Regulatory Impact
- Phase 0: Disambiguation
- Phase 1: Histone Modification & ChIP-seq
- Phase 2: Chromatin Accessibility & Architecture
- Phase 3: Regulatory Element Identification
- Phase 4: eQTL Analysis
- Phase 5: Gene Expression Context
- Phase 6: Transcription Factor Binding
- Phase 7: Variant Regulatory Scoring
- Phase 8: Integration
- Fallback Strategies
Files
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# Epigenomics and Chromatin Accessibility Research
## NOT for (use other skills instead)
- Methylation array data processing (CpG beta values, differential methylation) -> Use `tooluniverse-epigenomics`
- RNA-seq differential expression -> Use `tooluniverse-rnaseq-deseq2`
- GWAS variant interpretation -> Use `tooluniverse-gwas-snp-interpretation`
- Variant functional annotation from VCF -> Use `tooluniverse-variant-analysis`
---
## Reasoning: Classify the Question First
Before calling any tool, identify which question type you're answering. Each maps to a different tool set.View the complete source on GitHubtooluniverse-expression-data-retrieval
When your agent uses it
Retrieve gene expression and omics datasets from ArrayExpress and BioStudies with gene disambiguation and quality assessment. Use for finding RNA-seq/microarray datasets by organism/tissue/condition, comparing across studies (case-control, time-series, dose-response), and assessing dataset suitability before downloading. Always uses English search terms.
What it loads into context
Frontmatter
80 tokensAlways available to the agent
SKILL.md
1,895 tokensWhen the skill is activated
Reference documents
1,327 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- Workflow
- Phase 0: Clarification (When Needed)
- Phase 1: Query Disambiguation
- Phase 2: Data Retrieval (Internal)
- Phase 3: Report Dataset Profile
- Data Quality Tiers
- Reasoning Framework
- Error Handling
- Tool Reference
- Search Parameters
Files
CHECKLIST.mddoc1,400 bytesSKILL.mdcore8,045 bytesSKILL.mdcore8,045 bytesexamples.mddoc3,896 bytes
Read SKILL.md
# Gene Expression & Omics Data Retrieval
Retrieve gene expression experiments and multi-omics datasets with disambiguation and quality assessment.
**IMPORTANT**: Always use English terms in tool calls. Respond in the user's language.
**LOOK UP DON'T GUESS**: Never assume which datasets exist or their accessions. Always search to confirm.
## Domain Reasoning
Before retrieving, determine: organism, tissue, experimental design (case-control/time-series/dose-response). These affect which database to search and how to interpret results. RNA-seq provides wider dynamic range; microarray has extensive legacy data. Prioritize experiments with >=3 biological replicates, complete annotations, and both raw+processed data.
## Workflow
View the complete source on GitHubtooluniverse-fastq-qc
When your agent uses it
FASTQ quality control and adapter/quality-trimming decisions with local NGS tools — run FastQC on raw reads, summarize a project with MultiQC, interpret per-base sequence quality, per-base N content, adapter content, overrepresented sequences, sequence duplication and GC content, and decide whether (and how) to trim with fastp / Cutadapt before downstream analysis. seqkit for read counts/stats/subsampling. Use when someone asks "run QC on my FASTQs", "are my reads good quality?", "do I need to trim adapters?", "interpret this FastQC report", "what does this WARN/FAIL mean", "why are overrepresented sequences flagged", "should I quality-trim before alignment", "make a MultiQC summary", or "clean up these reads with fastp". NOT for differential expression / DEG analysis (use tooluniverse-rnaseq-deseq2), NOT for read alignment, coverage, or variant calling (use tooluniverse-variant-analysis / tooluniverse-sequence-analysis). Honest: shells out to real local binaries; if a tool is missing...
What it loads into context
Frontmatter
239 tokensAlways available to the agent
SKILL.md
2,954 tokensWhen the skill is activated
Reference documents
3,067 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to use vs. not
- Essential inputs to confirm
- Preflight (do this first, every time)
- Tool roles
- Bundled orchestration script
- INTERPRETATION — FastQC module -> meaning -> action
- Workflow
- Limitations (honest)
- Completeness checklist
- References
Scripts executed separately
scripts/run_fastq_qc.py
Files
SKILL.mdcore11,865 bytesSKILL.mdcore11,865 bytesSKILL.mdcore11,865 bytesSKILL.mdcore11,865 bytesreferences/fastqc_interpretation.mddoc5,426 bytesreferences/tools_and_install.mddoc3,402 bytesreferences/trimming_decisions.mddoc2,769 bytesscripts/run_fastq_qc.pyruns9,751 bytes
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# FASTQ Quality Control & Trimming Decisions
Run quality control on raw sequencing reads, interpret the report, and make
an evidence-based decision about whether to trim — using real local
command-line tools (FastQC, MultiQC, fastp, Cutadapt, seqkit).
## Honesty contract (read first)
This skill drives **real binaries**. It must never fabricate QC numbers.
1. **Preflight before anything.** Check whether the required tools are on
PATH. If a required tool is missing, emit the install plan and STOP.
Do not estimate, guess, or describe hypothetical QC results.
2. **Never auto-trim.** Trimming is a *decision*. QC-only is the default.View the complete source on GitHubtooluniverse-functional-genomics-screens
When your agent uses it
Interpret hits from CRISPR-KO/CRISPRi/shRNA screens by integrating DepMap essentiality, gnomAD constraint scores, pathway context (Reactome, STRING), druggability (DGIdb), and clinical evidence (CIViC, COSMIC). Use for screen-hit prioritization, essentiality ranking, and turning a list of screen hits into a prioritized target shortlist.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
1,378 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Workflow
- Phase Details
- Edge Cases
- Limitations
Files
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# Functional Genomics Screen Interpretation
Pipeline for validating and prioritizing hits from genetic screens (CRISPR-KO, CRISPRi, shRNA) by integrating essentiality (DepMap), constraint (gnomAD), pathways (Reactome, STRING), druggability (DGIdb), and clinical evidence (CIViC, COSMIC).
**Guiding principles**:
1. **Hits are hypotheses** -- screen results contain false positives; validate through orthogonal evidence
2. **Selectivity matters** -- pan-essential genes are poor drug targets; context-specific essentiality is high-value
3. **Pathway over gene** -- enriched pathways are more robust than individual hits
4. **Druggability is practical** -- prioritize chemically modulable targets
5. **English-first queries** -- use English gene names in tool calls
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first.
View the complete source on GitHubtooluniverse-gene-disease-association
When your agent uses it
Gene-disease association analysis across DisGeNET, OpenTargets, Monarch, OMIM, GenCC, Orphanet, Gene2Phenotype (G2P). Cross-references multiple sources for evidence-graded association reports with concordance scoring (5/5 sources agree → strong, 1/5 → weak). Use for 'which diseases is gene X associated with' or 'which genes cause disease Y' queries with quantitative confidence, including clinical-panel-based curation (developmental disorders, cancer, cardiac, skeletal, eye, skin panels).
What it loads into context
Frontmatter
127 tokensAlways available to the agent
SKILL.md
3,727 tokensWhen the skill is activated
Reference documents
16,053 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- Core Principles
- Workflow Overview
- Phase 1: Gene/Disease Identification & ID Resolution
- Phase 2: DisGeNET Associations
- Phase 3: OpenTargets Associations
- Phase 4: Monarch Initiative Associations
- Phase 5: Mendelian Disease Evidence
- Phase 6: Variant-Disease Associations (Optional)
- Phase 7: Evidence Synthesis
- Common Patterns
- Troubleshooting
- Resources
Files
../tooluniverse-disease-research/SKILL.mddoc10,463 bytes../tooluniverse-drug-target-validation/SKILL.mddoc19,387 bytesSKILL.mdcore14,771 bytesSKILL.mdcore14,771 bytesSKILL.mdcore14,771 bytesSKILL.mdcore14,771 bytesSKILL.mdcore14,771 bytesSKILL.mdcore14,771 bytesSKILL.mdcore14,771 bytesSKILL.mdcore14,771 bytes../tooluniverse-rare-disease-diagnosis/SKILL.mddoc8,871 bytes../tooluniverse-variant-interpretation/SKILL.mddoc25,719 bytes
Read SKILL.md
# Gene-Disease Association Analysis
Systematically query and compare gene-disease associations across 6+ databases to produce a unified, evidence-graded report. Cross-references DisGeNET scores, OpenTargets evidence, Monarch Initiative cross-species data, OMIM Mendelian mappings, GenCC curated validity, and Orphanet rare disease links.
**IMPORTANT**: Always use English gene names and disease terms in tool calls. Respond in the user's language.
---
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
---
## Core PrinciplesView the complete source on GitHubtooluniverse-gene-enrichment
When your agent uses it
Gene-set enrichment analysis — GO (Biological Process, Molecular Function, Cellular Component), KEGG, Reactome pathway enrichment via clusterProfiler, gseapy, ORA, GSEA, plus g:Profiler for enrichment/ID conversion/ortholog mapping/SNP-to-gene annotation. Use for interpreting DEG lists, screen hit lists, or any gene-list-to-pathways query. Includes simplify-cutoff handling and union-vs-total denominator conventions for percent-DE questions.
What it loads into context
Frontmatter
108 tokensAlways available to the agent
SKILL.md
8,177 tokensWhen the skill is activated
Reference documents
29,959 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- RULE ZERO — Check for pre-computed results FIRST
- PRIMARY SCRIPTS — use these FIRST
- Domain Reasoning: Background Selection
- When to Use This Skill
- Input Parameters
- Core Principles
- Decision Tree: ORA vs GSEA
- Decision Tree: gseapy vs ToolUniverse Tools
- Quick Start Workflow
- Evidence Grading
- Supported Organisms
- Common Patterns
- Troubleshooting
- Tool Reference
- Sample-Level and Alternative Scoring Methods
- Detailed Documentation
- Analysis conventions
- Resources
Scripts executed separately
scripts/condition_enrichment_screen.pyscripts/enrichgo_runner.pyscripts/format_enrichment_output.pyscripts/gseapy_enrichment_runner.py../../../../skills/tooluniverse-gene-enrichment/scripts/condition_enrichment_screen.py
Files
.env.templateasset499 bytesSKILL.mdcore32,190 bytesSKILL.mdcore32,190 bytesSKILL.mdcore32,190 bytesSKILL.mdcore32,190 bytesSKILL.mdcore32,190 bytesSKILL.mdcore32,190 bytesSKILL.mdcore32,190 bytesSKILL.mdcore32,190 bytesSKILL.mdcore32,190 bytesreferences/enrichr_guide.mddoc10,519 bytesreferences/gsea_workflow.mddoc12,633 bytesreferences/ora_workflow.mddoc11,934 bytesreferences/tool_parameters.mddoc11,778 bytesreferences/troubleshooting.mddoc14,417 bytesscripts/condition_enrichment_screen.pyruns12,245 bytesscripts/enrichgo_runner.pyruns10,332 bytesscripts/format_enrichment_output.pyruns14,945 bytesscripts/gseapy_enrichment_runner.pyruns15,892 bytes../tooluniverse-multiomic-disease-characterization/SKILL.mddoc8,883 bytes../tooluniverse-network-pharmacology/SKILL.mddoc14,171 bytes../tooluniverse-protein-interactions/SKILL.mddoc17,244 bytes../tooluniverse-spatial-omics-analysis/SKILL.mddoc12,616 bytes../../../../skills/tooluniverse-gene-enrichment/scripts/condition_enrichment_screen.pyruns12,245 bytes
Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Gene Enrichment and Pathway Analysis
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed enrichment files (CSV/TSV named `*enrich*`, `*go*`, `*kegg*`, `*reactome*`, `*ego*`, `*_simplified.csv`) → read directly
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running enrichment from raw DEG lists produces different numbers than the published answer due to subtle filter differences upstream, and is much slower.
View the complete source on GitHubtooluniverse-gene-liability
When your agent uses it
Evaluate the human safety liability of knocking down, knocking out, degrading, or pharmacologically inhibiting a gene. Use for gene safety scoring, on-target toxicity assessment, essentiality and genetic-constraint review, critical-organ expression analysis, or deciding whether a target needs partial, transient, or tissue-specific modulation.
What it loads into context
Frontmatter
75 tokensAlways available to the agent
SKILL.md
1,816 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Required Input
- Evidence Rules
- Workflow
- Required Output
Files
SKILL.mdcore8,351 bytesSKILL.mdcore8,351 bytes
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# Gene Liability Evaluation
Assess whether reducing a human gene's function is likely to be unsafe. Resolve the
gene, gather independent human and model-system evidence, calculate a transparent
0–100 liability score, and recommend an appropriate modulation strategy.
Higher scores mean greater predicted liability from the stated intervention. This is
a safety score, not a target-efficacy or druggability score.
## Required Input
Accept a gene symbol, name, Ensembl ID, or UniProt accession. Also capture:
- intervention modality: knockout, knockdown, degrader, irreversible inhibitor,View the complete source on GitHubtooluniverse-gene-regulatory-networks
When your agent uses it
Gene regulatory network analysis — TF-target inference (JASPAR motifs, ChIP-seq), motif scanning, eQTL integration, perturbation evidence (knockout/overexpression). Use for 'which TF regulates gene X', 'which genes does TF Y target', regulatory pathway reconstruction. Distinguishes direct (binding) vs indirect (co-expression) regulatory evidence.
What it loads into context
Frontmatter
91 tokensAlways available to the agent
SKILL.md
3,040 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- COMPUTE, DON'T DESCRIBE
- Workflow
- Common Mistakes
- Common Use Patterns
- Evidence Grading
Files
SKILL.mdcore11,357 bytes
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# Gene Regulatory Network Analysis
**GRN inference starts with: which TF regulates which gene?** Direct evidence (ChIP-seq binding) is stronger than indirect (co-expression correlation). A TF binding near a gene doesn't prove regulation — check if expression changes when the TF is perturbed. JASPAR provides binding motifs but motif presence in a promoter is only computational evidence (T3); ENCODE ChIP-seq data that places the TF at the locus in the relevant cell type is stronger (T1). eQTLs from GTEx show which variants affect expression but don't identify the upstream regulator — combine with TF motif disruption analysis for mechanistic insight.
**LOOK UP DON'T GUESS**: never assume JASPAR matrix IDs, Enrichr library names, or GTEx tissue identifiers — always search JASPAR by TF name and verify library names before calling enrichr.
## When to Use
Activate this skill when the user asks about:
- Transcription factor (TF) binding sites, motifs, or target genes
- Gene regulatory networks or transcriptional regulation
- Chromatin state and histone modifications in regulatory context
- TF-target relationships and co-regulation
- eQTL effects on gene regulationView the complete source on GitHubtooluniverse-glycobiology
When your agent uses it
Glycobiology and glycomics research using GlyGen — glycan structure lookup by GlyTouCan accession, glycan search by mass/monosaccharide-count/type, glycoprotein glycosylation profiles by UniProt accession, glycosylation-site-level detail (position, flanking sequence, attached glycan, co-located SNVs). Use when someone asks about "what glycans are attached to protein X", "N-linked vs O-linked glycosylation sites", "glycan mass/composition search", "GlyTouCan accession lookup", "glycosylation site at position N", or "which proteins have reported glycosylation in human/mouse". NOT for protein sequence/structure retrieval alone (use tooluniverse-sequence-retrieval or tooluniverse-protein-structure-retrieval), NOT for general small-molecule chemistry (use tooluniverse-chemical-compound-retrieval), NOT for post-translational modifications other than glycosylation (use tooluniverse-protein-modification-analysis).
What it loads into context
Frontmatter
233 tokensAlways available to the agent
SKILL.md
1,864 tokensWhen the skill is activated
Reference documents
2,060 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- Tool Chains for Common Questions
- Response Shape Notes (observed from live calls, not assumed from schema)
- Output
Files
SKILL.mdcore6,865 bytesreferences/glygen_tool_reference.mddoc6,416 bytes
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# Glycobiology and Glycomics (GlyGen)
Glycan structure and protein-glycosylation research using GlyGen's 5 ToolUniverse
tools: `GlyGen_get_glycan`, `GlyGen_search_glycans`, `GlyGen_get_glycoprotein`,
`GlyGen_search_glycoproteins`, `GlyGen_get_site`.
**LOOK UP, DON'T GUESS**: Glycan composition, mass, and site-level glycosylation
data are highly specific and not reliably recalled from memory — always resolve
through these tools rather than asserting a glycan's structure or a protein's
glycosylation status from general knowledge.
---
## When to Use This SkillView the complete source on GitHubtooluniverse-gpcr-structural-pharmacology
When your agent uses it
GPCR receptor pharmacology — agonist/antagonist/inverse-agonist/biased-agonist classification, GPCRdb structural data, receptor-ligand binding analysis, antibody-target interface (SAbDab). Use for GPCR drug discovery, biased-agonism analysis, receptor subtype selectivity questions, and orthosteric vs allosteric pocket characterization.
What it loads into context
Frontmatter
95 tokensAlways available to the agent
SKILL.md
3,549 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Tool Parameter Reference (CRITICAL)
- Workflow Overview
- Phase 1: GPCR Receptor Identification
- Phase 2: Ligand Landscape
- Phase 3: Structural Data
- Phase 4: Mutation Data
- Phase 5: Antibody Structure Retrieval
- Common Research Patterns
- Tool Combinations with Other Skills
- Fallback Chains
- Completeness Checklist
- Key References
Files
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# GPCR and Structural Pharmacology Research
**GPCR pharmacology**: agonist vs antagonist vs inverse agonist vs biased agonist — each has different clinical implications. Biased agonism (preferential G-protein vs β-arrestin signaling) can separate efficacy from side effects; for example, G-protein-biased opioid agonists aim to retain analgesia while reducing β-arrestin-mediated respiratory depression. Always classify retrieved ligands by their pharmacological type, not just their chemical structure. Receptor state (active vs inactive crystal structure) determines which ligands and mutations are interpretable — an inactive-state structure is appropriate for antagonist binding analysis, active-state for agonist-bound complexes. Generic GPCR numbering (Ballesteros-Weinstein) enables cross-receptor mutation comparison; always report positions in this system alongside sequence positions.
**LOOK UP DON'T GUESS**: never assume GPCRdb entry names (e.g., `adrb2_human`) or PDB IDs — always use `GPCRdb_list_proteins` to find the correct entry name and `GPCRdb_get_structures` to confirm available structures.
Research skill integrating GPCRdb (GPCR receptor biology), SAbDab (antibody structures), and PDBePISA (protein interface analysis) to support structural pharmacology, antibody engineering, and GPCR-targeted drug discovery.
**KEY PRINCIPLES**:
1. **Receptor-first** — Identify GPCR entry name before any GPCRdb queries
2. **Ligand classification** — Distinguish agonists, antagonists, partial agonists, biased agonists
3. **Structure-guided** — Pair GPCRdb mutation data with PDB structures via PDBePISA
4. **Antibody context** — Use SAbDab for therapeutic antibody structure retrieval and CDR analysis
5. **English-first queries** — Use standard receptor names (e.g., "beta-2 adrenergic receptor") in searches; convert to GPCRdb entry names for API callsView the complete source on GitHubtooluniverse-gwas-drug-discovery
When your agent uses it
Transform GWAS signals into drug targets and repurposing opportunities. Connects GWAS-significant loci to causal genes via fine-mapping/eQTL, then to druggable proteins via DGIdb/OpenTargets, then to existing drugs via ChEMBL. Use for GWAS-to-target hypothesis generation, druggable-fraction analysis of disease loci, and human-genetics-validated drug-repurposing prioritization.
What it loads into context
Frontmatter
99 tokensAlways available to the agent
SKILL.md
2,617 tokensWhen the skill is activated
Reference documents
12,126 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Reasoning Strategy
- Workflow Steps
- Quick Start
- All Tools by Category
- Best Practices
- Parameter Gotchas
- Interpretation: From GWAS Hit to Drug Target
- Troubleshooting
- Reference Files
Scripts executed separately
python_implementation.py
Files
EXAMPLES.mddoc2,119 bytesPROCEDURES.mddoc4,980 bytesQUICK_START.mddoc29,995 bytesREFERENCE.mddoc6,265 bytesREPORT_TEMPLATE.mddoc2,548 bytesSKILL.mdcore10,400 bytesSKILL.mdcore10,400 bytespython_implementation.pyruns14,635 bytes
Read SKILL.md
# GWAS-to-Drug Target Discovery
Transform genome-wide association studies (GWAS) into actionable drug targets and repurposing opportunities.
**IMPORTANT**: Always use English terms in tool calls. Respond in the user's language.
---
## Overview
This skill bridges genetic discoveries from GWAS with drug development by:
1. **Identifying genetic risk factors** - Finding genes associated with diseases
2. **Assessing druggability** - Evaluating which genes can be targeted by drugsView the complete source on GitHubtooluniverse-gwas-finemapping
When your agent uses it
Statistical fine-mapping of GWAS loci using credible sets (SuSiE, FINEMAP) and locus-to-gene scoring (Open Targets L2G). Identifies likely causal variants and target genes — distinct from positional 'nearest gene' which is often wrong. Use for prioritizing causal variants at GWAS hits, comparing fine-mapping methods, and converting lead SNPs to target genes.
What it loads into context
Frontmatter
96 tokensAlways available to the agent
SKILL.md
3,273 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- Overview
- Key Concepts
- Fine-Mapping Reasoning Framework (CRITICAL)
- Common Queries
- Tools Used
- Understanding Fine-Mapping Output
- Common Questions
- Limitations
- Best Practices
- References
- Related Skills
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesSKILL.mdcore13,380 bytespython_implementation.pyruns20,023 bytes
Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# GWAS Fine-Mapping & Causal Variant Prioritization
Identify and prioritize causal variants at GWAS loci using statistical fine-mapping and locus-to-gene predictions.
## Overview
Genome-wide association studies (GWAS) identify genomic regions associated with traits, but linkage disequilibrium (LD) makes it difficult to pinpoint the causal variant. **Fine-mapping** uses Bayesian statistical methods to compute the posterior probability that each variant is causal, given the GWAS summary statistics.
**REASONING STRATEGY — Start Here**:
Fine-mapping asks: which variant at this locus is CAUSAL? Work through this chain:
1. **LD structure first** — variants in high LD (r² > 0.8) cannot be statistically distinguished from each other. Look up the LD block via Open Targets or the GWAS Catalog before assuming any single variant is the cause.View the complete source on GitHubtooluniverse-gwas-snp-interpretation
When your agent uses it
Interpret a single GWAS SNP across multiple databases — GWAS Catalog hits, LD/haplotype context, eQTL evidence, regulatory annotation, ClinVar pathogenicity, gnomAD frequency. Use for 'what does this SNP do', SNP-to-mechanism tracing, and resolving lead-SNP-vs-causal-variant ambiguity. Always considers LD structure before claiming a SNP is mechanistically responsible.
What it loads into context
Frontmatter
98 tokensAlways available to the agent
SKILL.md
2,128 tokensWhen the skill is activated
Reference documents
1,037 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- What It Does
- Workflow
- Data Sources
- Input Parameters
- Output Format
- Example Usage
- Tools Used
- Interpretation Guide
- Limitations
- Best Practices
- Technical Notes
- Related Skills
- References
- Version
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesFINAL_VERIFICATION.txtdoc4,872 bytesQUICK_START.mddoc3,719 bytesSKILL.mdcore8,232 bytesSKILL.mdcore8,232 bytesSKILL.mdcore8,232 bytesSKILL.mdcore8,232 bytespython_implementation.pyruns11,000 bytes
Read SKILL.md
# GWAS SNP Interpretation Skill
**SNP interpretation**: a GWAS hit is a REGION, not a single causal variant. The lead SNP may not be causal — it may be in LD with the causal variant. Always check LD structure and functional annotation before concluding a specific SNP is mechanistically responsible. Use `LDlink_get_proxies(variant="rs...", population="EUR")` to retrieve the high-R² LD proxies (needs a free LDLINK_TOKEN) — a proxy in a coding/regulatory region is a better mechanistic candidate than the lead SNP itself. Fine-mapping (SuSiE, FINEMAP credible sets) narrows the causal set but rarely identifies a single variant with certainty. L2G scores integrate eQTL, chromatin interaction, and distance data to predict the causal gene — a lead SNP mapping to gene A may actually regulate gene B 500 kb away via a distal enhancer.
**LOOK UP DON'T GUESS**: never assume a SNP's functional consequence, mapped gene, or population frequency — always call `gwas_get_snp_by_id` and `OpenTargets_get_variant_info` to retrieve current annotations.
## Overview
Interpret genetic variants (SNPs) from GWAS studies by aggregating evidence from multiple sources to provide comprehensive clinical and biological context.
**Use Cases:**
- "Interpret rs7903146" (TCF7L2 diabetes variant)
- "What diseases is rs429358 associated with?" (APOE Alzheimer's variant)
- "Clinical significance of rs1801133" (MTHFR variant)View the complete source on GitHubtooluniverse-gwas-study-explorer
When your agent uses it
Compare GWAS studies, perform meta-analyses across cohorts, and assess signal replication. Uses GWAS Catalog metadata, study-level statistics, and cross-cohort comparison. Use for evaluating GWAS reproducibility for a trait, meta-analysis sample size and effect-size aggregation, and detecting study heterogeneity (population, design, ancestry).
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
2,458 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- COMPUTE, DON'T DESCRIBE
- Domain Reasoning: Comparing Studies for the Same Trait
- Use Cases
- Statistical Methods
- Study Quality Assessment
- Best Practices
- Limitations & Caveats
- Tools Used
- Glossary
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesSKILL.mdcore11,109 bytespython_implementation.pyruns24,517 bytes
Read SKILL.md
# GWAS Study Deep Dive & Meta-Analysis
**Compare GWAS studies, perform meta-analyses, and assess replication across cohorts**
---
## Overview
The GWAS Study Deep Dive & Meta-Analysis skill enables comprehensive comparison of genome-wide association studies (GWAS) for the same trait, meta-analysis of genetic loci across studies, and systematic assessment of replication and study quality. It integrates data from the NHGRI-EBI GWAS Catalog and Open Targets Genetics to provide a complete picture of the genetic architecture of complex traits.
### Key Capabilities
1. **Study Comparison**: Compare all GWAS studies for a trait, assessing sample sizes, ancestries, and platformsView the complete source on GitHubtooluniverse-gwas-trait-to-gene
When your agent uses it
Discover causal genes for diseases/traits from GWAS data using Open Targets L2G (locus-to-gene) scoring — integrates eQTL, chromatin interaction, and distance evidence. Use for trait-to-gene mapping, drug-target hypothesis generation from GWAS, and replacing the 'nearest gene' heuristic with multi-evidence L2G scores.
What it loads into context
Frontmatter
89 tokensAlways available to the agent
SKILL.md
3,213 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Overview
- Use Cases
- Workflow
- Key Concepts
- Required ToolUniverse Tools
- Parameters
- Output Schema
- Example Results
- Best Practices
- Tool-Specific Notes (Updated)
- Programmatic Access (Beyond Tools)
- Limitations
- Related Skills
- Data Sources
- Citation
- Support
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesSKILL.mdcore12,084 bytesSKILL.mdcore12,084 bytesSKILL.mdcore12,084 bytesSKILL.mdcore12,084 bytesSKILL.mdcore12,084 bytespython_implementation.pyruns8,905 bytes
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# GWAS Trait-to-Gene Discovery
**Nearest gene is often wrong.** Use L2G (locus-to-gene) scores from Open Targets which integrate eQTL, chromatin interaction, and distance data. L2G > 0.5 is a strong prediction; positional mapping alone should not be used to claim a causal gene. A single GWAS study with p < 5e-8 is suggestive — replication across independent cohorts is required for high confidence. GWAS hits are associations in the studied population; effect sizes and even the implicated gene can differ across ancestries due to differing LD patterns. Treat gene lists from GWAS as ranked candidates for validation, not confirmed causal genes.
**LOOK UP DON'T GUESS**: never assume trait-to-gene mappings or L2G scores — always call `gwas_search_associations` and `OpenTargets_get_study_credible_sets` to retrieve current data; associations are updated as new GWAS are published.
**Discover genes associated with diseases and traits using genome-wide association studies (GWAS)**
## Overview
This skill enables systematic discovery of genes linked to diseases/traits by analyzing GWAS data from two major resources:
- **GWAS Catalog** (EBI/NHGRI): Curated catalog of published GWAS with >500,000 associations
- **Open Targets Genetics**: Fine-mapped GWAS signals with locus-to-gene (L2G) predictions
View the complete source on GitHubtooluniverse-hla-immunogenomics
When your agent uses it
HLA gene-family analysis and MHC-peptide binding for transplant compatibility, vaccine epitope coverage, and cancer immunotherapy. Uses IMGT (HLA polymorphism), IEDB (epitope-MHC binding), UniProt (annotation), DGIdb (druggability). Use for HLA typing/imputation review, vaccine HLA coverage, and immunotherapy prediction biomarkers (HLA-LOH, neoantigen presentation).
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
4,080 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Databases
- Workflow Overview
- Phase Details
- Edge Cases & Fallbacks
- Limitations
Files
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# HLA & Immunogenomics Analysis
Pipeline for exploring HLA gene families, MHC-peptide binding, epitope associations, and their clinical implications in transplantation, vaccine development, and cancer immunotherapy. Bridges immunogenetic databases (IMGT, IEDB) with functional annotation (UniProt) and druggability data (DGIdb).
## Reasoning Strategy
HLA analysis is fundamentally about peptide presentation: the polymorphism of HLA molecules determines which peptides are displayed to T cells, which in turn governs disease susceptibility, transplant rejection, drug hypersensitivity, and vaccine immunogenicity. HLA type affects disease susceptibility for autoimmune conditions (HLA-B27 and ankylosing spondylitis), transplant rejection (HLA mismatch drives alloresponse), drug hypersensitivity (abacavir causes severe hypersensitivity reactions only in HLA-B*57:01 carriers), and vaccine design (epitopes must be presented by the recipient's HLA alleles to elicit a T-cell response). Class I and Class II HLA molecules have fundamentally different binding grooves, peptide lengths, and T-cell partners — never conflate them. The absence of an epitope from IEDB means it has not been tested, not that it cannot bind.
**LOOK UP DON'T GUESS**: Never assume an allele's binding properties or population frequency — query IEDB for experimental binding data and IMGT for allele annotation. Do not guess which HLA alleles are common in a population; look up published frequency data via PubMed.
**Guiding principles**:
1. **HLA nomenclature precision** -- HLA allele names follow strict conventions (e.g., HLA-A*02:01); get the resolution level right
2. **MHC class awareness** -- Class I (A, B, C) and Class II (DR, DQ, DP) have different binding properties and clinical roles
3. **Species context** -- most queries target human HLA, but MHC exists across vertebrates; confirm species earlyView the complete source on GitHubtooluniverse-image-analysis
When your agent uses it
Microscopy and quantitative imaging analysis — colony morphometry, fluorescence intensity quantification, cell-count statistics, dose-response curves, and ANOVA/Dunnett on image-derived measurements. Uses pandas/numpy/scipy/scikit-image. Use for analyzing tabular outputs from CellProfiler/ImageJ, image-derived measurement statistics, and image-based assay quantification.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
3,201 tokensWhen the skill is activated
Reference documents
23,295 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- RULE ZERO — Check for pre-computed results FIRST
- CRITICAL — "Relative proportion of A to B" defaults to PERCENTAGE
- LOOK UP, DON'T GUESS
- When to Use
- Core Principles
- Required Packages
- Workflow Decision Tree
- Analysis Workflow
- Common Patterns
- Raw Image Processing
- R-to-Python Equivalents
- Answer Formatting
- Evidence Grading
- References
Scripts executed separately
scripts/batch_process.pyscripts/measure_fluorescence.pyscripts/segment_cells.py
Files
SKILL.mdcore12,582 bytesSKILL.mdcore12,582 bytesSKILL.mdcore12,582 bytesSKILL.mdcore12,582 bytesSKILL.mdcore12,582 bytesreferences/cell_counting.mddoc15,226 bytesreferences/fluorescence_analysis.mddoc14,692 bytesreferences/image_processing.mddoc11,945 bytesreferences/segmentation.mddoc17,933 bytesreferences/statistical_analysis.mddoc22,795 bytesreferences/troubleshooting.mddoc10,053 bytesscripts/batch_process.pyruns8,869 bytesscripts/measure_fluorescence.pyruns7,217 bytesscripts/segment_cells.pyruns7,588 bytes
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# Microscopy Image Analysis and Quantitative Imaging Data
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
---
## CRITICAL — "Relative proportion of A to B" defaults to PERCENTAGEView the complete source on GitHubtooluniverse-immune-repertoire-analysis
When your agent uses it
TCR/BCR repertoire analysis — V(D)J segment usage, CDR3 sequence diversity, clonality scoring, antigen specificity matching to IEDB, public-clone identification. Use for adaptive immune response characterization, post-treatment immune monitoring, antigen-specific clone tracking, and clonal-expansion analysis in immunotherapy or vaccination studies.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
2,945 tokensWhen the skill is activated
Reference documents
5,555 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- Overview
- Core Workflow
- ToolUniverse Tool Integration
- Quick Start
- Reasoning Framework for Result Interpretation
- References
- See Also
Files
ANALYSIS_DETAILS.mddoc15,485 bytesSKILL.mdcore12,198 bytesSKILL.mdcore12,198 bytesSKILL.mdcore12,198 bytesUSE_CASES.mddoc5,521 bytes
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# ToolUniverse Immune Repertoire Analysis
Comprehensive skill for analyzing T-cell receptor (TCR) and B-cell receptor (BCR) repertoire sequencing data to characterize adaptive immune responses, clonal expansion, and antigen specificity.
## Domain Reasoning
Repertoire diversity reflects immune history. High clonality — a few clones dominating — indicates antigen-driven expansion, as seen in active infection, tumor-infiltrating lymphocytes, or chronic stimulation. Low diversity points to immunodeficiency or treatment-induced lymphopenia. Always compare observed metrics against healthy donor reference distributions before drawing conclusions; a Shannon entropy of 7 is unremarkable in a healthy adult but alarming post-chemotherapy.
## LOOK UP DON'T GUESS
- Clonotype frequency thresholds, CDR3 length ranges, and convergence ratios: query IEDB or VDJdb; do not assume values from memory.
- Epitope specificities for expanded clones: search `iedb_search_tcell_assays` and `BVBRC_search_epitopes`; never infer antigen identity from CDR3 alone.
- V gene family usage biases in healthy donors: retrieve published reference data or query ImmPort; do not assume baseline distributions are uniform.
- Sequencing depth adequacy: compute rarefaction curves from the actual data; do not guess whether depth is sufficient.View the complete source on GitHubtooluniverse-immunology
When your agent uses it
Immunology research workflows: antibody-antigen interactions, T/B cell repertoire, MHC/HLA binding prediction, autoimmune disease genetics, vaccine epitope mapping. Uses IEDB, IMGT, SAbDab, UniProt. Use for adaptive immunity questions, immune response analysis, antibody/TCR/BCR characterization, immunogenicity prediction, and immune-pathway-to-disease mapping.
What it loads into context
Frontmatter
93 tokensAlways available to the agent
SKILL.md
3,613 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Reasoning Frameworks
- Tool Reference
- Parameter Gotchas
- Workflows
Files
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# Immunology Research Skill
**KEY PRINCIPLES**: Multi-layer evidence; source every claim; use immunology-specific databases first (IEDB, IMGT, SAbDab); always use English gene/protein names in tool calls.
---
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first (PubMed, UniProt, ChEMBL, ClinVar, etc.) rather than reasoning from memory. A database-verified answer is always more reliable than a guess.
**For MC about immune mechanisms**: Look up the specific pathway/receptor/cytokine before answering. Use `PubMed_search_articles` with the exact terms from the question. The answer is almost always in the first few search results.
**Specific LOOK UP targets** (never guess these):
- **Immune cell markers**: CD markers for cell subsets (e.g., Treg = CD4+CD25+FOXP3+, not just "CD4+"). Query UniProt or IEDB.
- **Cytokine functions**: IL-17 is pro-inflammatory (Th17), IL-10 is anti-inflammatory (Treg) — but context matters. Verify via KEGG pathway or PubMed.View the complete source on GitHubtooluniverse-immunotherapy-response-prediction
When your agent uses it
Predict patient response to immune checkpoint inhibitors (ICIs) by integrating tumor mutational burden (TMB), microsatellite instability (MSI), PD-L1 expression, HLA status, and immune-related gene expression. Outputs ICI Response Score with drug-specific recommendations and resistance-risk assessment. Use for melanoma/NSCLC/RCC immunotherapy decision support.
What it loads into context
Frontmatter
87 tokensAlways available to the agent
SKILL.md
2,743 tokensWhen the skill is activated
Reference documents
7,915 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parsing
- Workflow Overview
- Phase 1: Input Standardization & Cancer Context
- Phase 2: TMB Analysis
- Phase 3: Neoantigen Analysis
- Phase 4: MSI/MMR Status Assessment
- Phase 5: PD-L1 Expression Analysis
- Phase 6: Immune Microenvironment Profiling
- Phase 7: Mutation-Based Predictors
- Phase 8: Clinical Evidence & ICI Options
- Phase 9: Resistance Risk Assessment
- Phase 10: Multi-Biomarker Score Integration
- Phase 11: Clinical Recommendations
- Output Report
- Tool Parameter Reference
- Evidence Tiers
- References
Files
EXAMPLES.mddoc4,807 bytesINPUT_REFERENCE.mddoc1,585 bytesREPORT_TEMPLATE.mddoc5,146 bytesSCORING_TABLES.mddoc7,341 bytesSKILL.mdcore10,172 bytesSKILL.mdcore10,172 bytesTOOLS_REFERENCE.mddoc6,406 bytes
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# Immunotherapy Response Prediction
Predict patient response to immune checkpoint inhibitors (ICIs) using multi-biomarker integration. Transforms a patient tumor profile (cancer type + mutations + biomarkers) into a quantitative ICI Response Score with drug-specific recommendations, resistance risk assessment, and monitoring plan.
## Reasoning Before Searching
Not all tumors respond to checkpoint inhibitors. Reason through the biology before running tools:
- **TMB (tumor mutational burden)**: More somatic mutations produce more neoantigens, which are recognized by T cells. High TMB (>=10 mut/Mb, FDA-approved threshold for pembrolizumab) generally predicts better response — but this varies by cancer type (e.g., RCC responds despite low TMB).
- **MSI-H (microsatellite instability-high)**: Caused by defective DNA mismatch repair (MMR). MSI-H tumors have very high TMB and are pan-cancer approved for pembrolizumab. Check MLH1, MSH2, MSH6, PMS2 mutations.
- **PD-L1 expression**: The direct target of pembrolizumab/atezolizumab. High PD-L1 (TPS >=50% or CPS >=10 depending on cancer) predicts response in some cancers (NSCLC) but not all (melanoma, where TMB is more predictive).
- **Resistance factors** are equally important: STK11, KEAP1, JAK1/2 loss, B2M mutations can render an otherwise TMB-high tumor non-responsive.
Before calling any tool, determine which biomarkers are available for this patient and which are unknown. This determines which phases can be scored with data vs. must use cancer-type priors. Do not default to "moderate" for unknowns — flag them explicitly as missing.View the complete source on GitHubtooluniverse-infectious-disease
When your agent uses it
Rapid pathogen characterization and drug repurposing for outbreaks. Combines pathogen genomics (NCBI, BVBRC), host immune response (IEDB), drug-target databases (ChEMBL, DGIdb), and literature surveillance (PubMed/EuropePMC). Use for emerging-pathogen profiling, antiviral candidate identification, and outbreak intelligence reporting.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
2,909 tokensWhen the skill is activated
Reference documents
12,105 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Critical Workflow Requirements
- Phase 0: Tool Verification
- Workflow Overview
- Phase Summaries
- Evidence Grading
- Completeness Checklist
- Fallback Chains
- References
Files
CHECKLIST.mddoc6,652 bytesEXAMPLES.mddoc13,195 bytesSKILL.mdcore12,224 bytesSKILL.mdcore12,224 bytesTOOLS_REFERENCE.mddoc15,790 bytesphase_details.mddoc7,793 bytesreport_template.mddoc2,450 bytes
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Infectious Disease Outbreak Intelligence
Rapid response system for emerging pathogens using taxonomy analysis, target identification, structure prediction, and computational drug repurposing.
**KEY PRINCIPLES**:
1. **Speed is critical** - Optimize for rapid actionable intelligence
2. **Target essential proteins** - Focus on conserved, essential viral/bacterial proteins
3. **Leverage existing drugs** - Prioritize FDA-approved compounds for repurposing
4. **Structure-guided** - Use NvidiaNIM for rapid structure prediction and docking
5. **Evidence-graded** - Grade repurposing candidates by evidence strength
6. **Actionable output** - Prioritized drug candidates with rationaleView the complete source on GitHubtooluniverse-inorganic-physical-chemistry
When your agent uses it
Inorganic chemistry, physical chemistry, and materials science — crystal structures, coordination chemistry, lattice parameters, thermodynamic properties, electronic structure. Use for unit cell volume calculations, coordination geometry, materials property estimation, and inorganic-mechanism reasoning. Complementary to tooluniverse-organic-chemistry.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
2,261 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- Available Tools
- LOOK UP DON'T GUESS
Scripts executed separately
scripts/equilibrium_solver.py../../../../skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.py../../../../skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.py../../../../skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.py../../../../skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.py../../../../skills/tooluniverse-organic-chemistry/scripts/crystal_validator.py
Files
SKILL.mdcore8,322 bytesSKILL.mdcore8,322 bytesscripts/equilibrium_solver.pyruns8,373 bytes../../../../skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.pyruns8,373 bytes../../../../skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.pyruns8,373 bytes../../../../skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.pyruns47,120 bytes../../../../skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.pyruns47,120 bytes../../../../skills/tooluniverse-organic-chemistry/scripts/crystal_validator.pyruns7,659 bytes
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# Inorganic & Physical Chemistry
## Reasoning Strategy
### 1. Crystal Structure Questions
**When given crystal structure data**, always COMPUTE don't guess:
1. **Calculate unit cell volume** for the crystal system:
- Cubic: V = a^3
- Tetragonal: V = a^2 * c
- Orthorhombic: V = a * b * c
- Monoclinic: V = a * b * c * sin(beta)
- Triclinic: V = a*b*c * sqrt(1 - cos^2(alpha) - cos^2(beta) - cos^2(gamma) + 2*cos(alpha)*cos(beta)*cos(gamma))
- Hexagonal: V = a^2 * c * sqrt(3)/2
View the complete source on GitHubtooluniverse-install-skills
When your agent uses it
Detect and auto-install missing ToolUniverse research skills. Checks common Claude Code/Cursor/Codex skill directories for the canary file, and installs any missing skills if none found. Use when the plugin's research skills aren't loading, when migrating between clients, or when verifying a skill installation.
What it loads into context
Frontmatter
71 tokensAlways available to the agent
SKILL.md
901 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Detection
- Installation
- Client Detection
- After Installation
Files
SKILL.mdcore3,114 bytesSKILL.mdcore3,114 bytesSKILL.mdcore3,114 bytesSKILL.mdcore3,114 bytes
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# ToolUniverse Install Skills
Checks whether the ToolUniverse specialized skills are installed and installs them automatically if not.
## Detection
Use the Shell tool to check for the canary file across all common client locations:
```bash
ls .cursor/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \
|| ls .agents/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \
|| ls .windsurf/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \
|| ls .gemini/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \
|| ls .claude/skills/tooluniverse-drug-research/SKILL.md 2>/dev/null \View the complete source on GitHubtooluniverse-kegg-disease-drug
When your agent uses it
KEGG-based disease-drug-variant network research. Connects diseases to causal genes, drugs to molecular targets, and variants to pathways using KEGG's editorially curated databases (KEGG Disease, Drug, Network, Variant, Pathway). Use for drug repurposing via shared pathways, mechanistic disease-gene-drug networks, and pathway-based target discovery. Distinguishes direct (binding) vs indirect (pathway co-membership) drug-target relationships.
What it loads into context
Frontmatter
169 tokensAlways available to the agent
SKILL.md
2,733 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- When to Use
- Tool Inventory (12 tools)
- Workflow
- Example Workflow: Find Drugs Targeting BRAF Variants in Cancer
- ID Conversion & Cross-Linking
- Integration with Other Skills
- Reasoning Framework for Result Interpretation
- Output
Files
SKILL.mdcore11,131 bytesSKILL.mdcore11,131 bytesSKILL.mdcore11,131 bytesSKILL.mdcore11,131 bytesSKILL.mdcore11,131 bytesSKILL.mdcore11,131 bytesSKILL.mdcore11,131 bytesSKILL.mdcore11,131 bytesSKILL.mdcore11,131 bytesSKILL.mdcore11,131 bytes
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# KEGG Disease-Drug-Variant Research
Systematic exploration of disease-drug-variant relationships using KEGG's curated databases.
## Reasoning Strategy
KEGG maps diseases to pathways and drugs to targets, but the real value is in the connections — which pathways link a disease gene to a drug target? This is a network question, not a simple lookup. A gene appearing in a KEGG disease entry has been editorially reviewed as mechanistically relevant; a drug entry with a confirmed target is more reliable than one inferred from pathway co-membership. When using KEGG for drug repurposing, always ask: is the drug-target relationship direct (the drug binds the gene product) or indirect (the drug affects a pathway that contains the gene)? Direct relationships are far stronger evidence. KEGG coverage is not exhaustive — absence from KEGG does not mean absence of biological involvement; complement with Reactome, WikiPathways, or CTD for broader coverage. ID namespace differences are a frequent source of errors: KEGG uses its own gene IDs (e.g., hsa:7157 for TP53), so always convert external IDs before querying KEGG-specific tools.
**LOOK UP DON'T GUESS**: Do not assume KEGG disease IDs, drug IDs, or gene IDs from memory — always search first with `KEGG_search_disease`, `KEGG_search_drug`, or `KEGG_convert_ids`. Do not assume which pathways link a disease gene to a drug; use `KEGG_link_entries` and `KEGG_get_network` to retrieve the actual connections.
## When to Use
- "What genes are associated with [disease] in KEGG?"
- "Find KEGG drugs targeting [gene/pathway]"View the complete source on GitHubtooluniverse-lab-data-standardization
When your agent uses it
Convert lab instrument output files (CSV, TSV, Excel, PDF — plate readers, spectrophotometers like NanoDrop, qPCR instruments, cell counters, chromatography systems) into a standardized Allotrope-Simple-Model-inspired JSON document plus a flattened 2D CSV, for LIMS/ELN upload or downstream analysis. Use when someone asks to "convert this instrument file", "standardize this lab data", "prepare this for LIMS upload", "what format is this plate reader/NanoDrop/qPCR output in", "turn this CSV into ASM/Allotrope format", or "separate the raw and calculated values in this instrument export". NOT for general data wrangling of arbitrary tabular data unrelated to lab instruments (use tooluniverse-data-wrangling), NOT for genomics/proteomics-specific file formats like FASTQ/VCF/mzML (use tooluniverse-fastq-qc, tooluniverse-variant-analysis, or tooluniverse-proteomics-analysis). Honest: never fabricates a measurement value; if a vendor's native parser (allotropy) is unavailable it says so and...
What it loads into context
Frontmatter
248 tokensAlways available to the agent
SKILL.md
1,636 tokensWhen the skill is activated
Reference documents
1,942 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to use vs. not
- Two-tier conversion strategy
- Workflow
- Options reference (`convert_instrument_data.py`)
- References
Scripts executed separately
scripts/convert_instrument_data.pyscripts/flatten_to_csv.pyscripts/validate_output.py
Files
SKILL.mdcore6,716 bytesSKILL.mdcore6,716 bytesSKILL.mdcore6,716 bytesSKILL.mdcore6,716 bytesSKILL.mdcore6,716 bytesreferences/field_classification_guide.mddoc2,774 bytesreferences/schema_overview.mddoc2,695 bytesreferences/supported_instruments.mddoc2,555 bytesscripts/convert_instrument_data.pyruns14,410 bytesscripts/flatten_to_csv.pyruns1,703 bytesscripts/validate_output.pyruns4,800 bytes
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# Lab Instrument Data Standardization
Convert raw instrument output into a standardized, structured format —
distinguishing raw measurements from calculated/derived values with
explicit traceability — for LIMS/ELN upload, data lakes, or downstream
analysis.
## Honesty contract (read first)
1. **Never fabricate a value.** Every number in the output JSON/CSV must
come directly from the source file. If a value can't be found or
confidently parsed, it is omitted — never guessed.
2. **Never claim certified ASM output you didn't produce.** Tier 1 (native
`allotropy` parsing) produces genuine Allotrope Simple Model documents.View the complete source on GitHubtooluniverse-lab-protocols
When your agent uses it
Find and inspect published, peer-reviewed or community lab protocols from protocols.io — search by technique/keyword, pull a protocol's full description and materials list, or get its ordered step-by-step instructions. Use when someone asks to "find a protocol for X", "how do others do [technique]", "get the steps for protocol [ID/DOI]", or "check the materials list for this method" before designing or running a wet-lab experiment. NOT for designing a novel cloning/assembly strategy from scratch (use tooluniverse-molecular-cloning), NOT for primer design (use tooluniverse-primer-design). Honest: protocols.io requires an OAuth Bearer token (PROTOCOLS_IO_API_KEY) for every call, including public reads — without it these tools are blocked, not degraded.
What it loads into context
Frontmatter
180 tokensAlways available to the agent
SKILL.md
1,085 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to Use This Skill
- Workflow
- Notes
Files
SKILL.mdcore4,728 bytes
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# Lab Protocol Discovery (protocols.io)
Find a published, step-by-step lab protocol before designing your own from scratch, or pull the exact steps/materials for a protocol someone already pointed you to.
## Honesty contract (read first)
[Credential-related source omitted]
2. **Never fabricate a protocol.** If the key is missing or a lookup fails, say so — do not describe a plausible-sounding protocol from memory and present it as if it came from protocols.io.
3. **Tokens expire (~1 year).** A previously-working key that starts failing with an auth error may simply need refreshing, not re-registering from scratch.
## When to Use This Skill
Apply when users:
- Want to find a published protocol for a specific technique before running it themselves (e.g., "is there a protocols.io method for lentiviral titering?")View the complete source on GitHubtooluniverse-lipidomics
When your agent uses it
Lipid analysis and lipid-disease associations using LIPID MAPS classification, HMDB metabolite data, KEGG/Reactome lipid pathways (sphingolipid, eicosanoid, steroid, fatty acid), and PubChem chemical info. Use for lipid identification, lipid metabolism pathway mapping, and lipid-associated disease analysis (cardiovascular, diabetes, NAFLD).
What it loads into context
Frontmatter
90 tokensAlways available to the agent
SKILL.md
3,113 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- When to Use
- Core Tools
- Workflow
- Limitations
Files
SKILL.mdcore11,937 bytesSKILL.mdcore11,937 bytesSKILL.mdcore11,937 bytesSKILL.mdcore11,937 bytes
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# Lipidomics Analysis
Integrated pipeline for lipid identification, classification, pathway mapping, and disease association analysis. Distinct from general metabolomics because lipids have unique classification systems (LIPID MAPS), specialized pathways (sphingolipid, eicosanoid, steroid), and disease associations (cardiovascular, neurodegeneration, metabolic syndrome).
## Reasoning Strategy
Lipid identification starts with mass spectrometry: the lipid class is determined by the head group fragment mass (e.g., m/z 184 for phosphocholine in positive mode), total chain length and saturation from the precursor exact mass, and individual fatty acid chains from neutral loss or product ion scans. LIPID MAPS classification organizes lipids by chemical structure into 8 categories — knowing the category immediately tells you the likely biological context (sphingolipids → apoptosis/neurodegeneration; glycerophospholipids → membrane remodeling; eicosanoids → inflammation). Structural specificity matters biologically: Cer(d18:1/16:0) and Cer(d18:1/24:1) have different membrane properties and disease associations despite being the same lipid class. Always map changed lipids back to metabolic pathways because lipids are intermediates — an elevated ceramide could mean increased synthesis (CERS activity up), decreased degradation (ASAH1 down), or shunting from sphingomyelin (SMPD1 up).
**LOOK UP DON'T GUESS**: Do not assume a lipid's LIPID MAPS ID, exact mass, or pathway membership — query `LipidMaps_search_by_name` first. Do not guess which diseases are associated with a lipid class; retrieve them from HMDB or CTD.
**Key principles**:
1. **LIPID MAPS classification first** — use the 8-category system (fatty acyls, glycerolipids, glycerophospholipids, sphingolipids, sterol lipids, prenol lipids, saccharolipids, polyketides)
2. **Structural specificity matters** — chain length, unsaturation, and sn-position affect biological function
3. **Connect to pathways** — lipids are metabolic intermediates; always map to biosynthesis/degradation pathwaysView the complete source on GitHubtooluniverse-literature-deep-research
When your agent uses it
Deep literature review — PubMed, EuropePMC, bioRxiv preprints, citation networks, evidence synthesis. Disambiguates queries, runs collision-aware searches, grades evidence T1-T4, and produces structured reports. Use for systematic literature review, meta-analysis evidence collection, and detailed answer-with-citations workflows.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
4,153 tokensWhen the skill is activated
Reference documents
21,105 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Workflow
- Phase 0: Mode Selection
- Phase 1: Subject Disambiguation + Profile
- Phase 2: Literature Search
- Phase 3: Evidence Grading
- Report Output
- Communication
- References
Files
EXAMPLES.mddoc26,860 bytesFULLTEXT_STRATEGY.mddoc3,807 bytesQUICK_REFERENCE.mddoc6,208 bytesREPORT_TEMPLATE.mddoc12,892 bytesSKILL.mdcore17,034 bytesSKILL.mdcore17,034 bytesSKILL.mdcore17,034 bytesSKILL.mdcore17,034 bytesSKILL.mdcore17,034 bytesTOOL_NAMES_REFERENCE.mddoc22,206 bytesWORKFLOW.mddoc7,827 bytes
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# Literature Deep Research
Systematic literature research: disambiguate, search with collision-aware queries, grade evidence, produce structured reports.
**KEY PRINCIPLES**: (1) Disambiguate first (2) Right-size deliverable (3) Grade every claim T1-T4 (4) All sections mandatory even if "limited evidence" (5) Source attribution for every claim (6) English-first queries, respond in user's language (7) Report = deliverable, not search log
---
## LOOK UP, DON'T GUESS
Search PubMed/EuropePMC FIRST before reasoning. A published paper beats memory.
**Factoid search strategy:**
1. Extract KEY TERMS (most specific nouns/verbs)View the complete source on GitHubtooluniverse-medical-imaging-radiology
When your agent uses it
Discover and characterize cancer radiology imaging cohorts from The Cancer Imaging Archive (TCIA) — list public collections (e.g. LIDC-IDRI, TCGA-GBM, TCGA-LUAD, TCGA-BRCA), enumerate patients/subjects and studies within a collection, browse DICOM series by modality (CT, MR, PT, CR, DX, US, NM) or body part (CHEST, BRAIN, ABDOMEN, ...), pull series-level DICOM metadata and scanner manufacturer, estimate download size before pulling data, and enumerate individual DICOM instance (slice) UIDs within a series. Use when someone asks to "find a CT/MRI/PET imaging cohort for [cancer type]", "how many patients/studies/series are in [TCIA collection]", "what imaging modalities/body parts/scanners does [collection] have", "look up DICOM series metadata for [SeriesInstanceUID]", "estimate the size of this TCIA series before downloading", or "build a radiology imaging research cohort". NOT for microscopy/cell imaging quantification (use tooluniverse-image-analysis), NOT for actual DICOM pixel...
What it loads into context
Frontmatter
256 tokensAlways available to the agent
SKILL.md
1,806 tokensWhen the skill is activated
Reference documents
2,468 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Scope: what this skill can and cannot do
- When to Use This Skill
- Tool Reference
- Workflow
- Notes
- References
Files
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# Cancer Radiology Imaging Cohort Discovery (TCIA)
Discover and characterize public cancer imaging cohorts from The Cancer
Imaging Archive (TCIA, National Cancer Institute) — collection, patient,
study, and DICOM series-level metadata for building or scoping a radiology
research cohort.
## Scope: what this skill can and cannot do
**Can:** enumerate collections, patients, studies, and series; filter by
modality/body part/manufacturer; pull DICOM series-level metadata (scanner,
acquisition, license); estimate series download size; list individual DICOM
instance (slice) UIDs.
View the complete source on GitHubtooluniverse-mendelian-randomization
When your agent uses it
Mendelian randomization (MR) causal inference — does an exposure, risk factor, or biomarker CAUSALLY affect a disease/outcome, using genetic variants as instrumental variables (IEU OpenGWAS / EpiGraphDB MR-EvE). Use this whenever the user asks if X causes Y, whether an observational association is actually causal or just correlation, if a biomarker/trait is a causal risk factor, wants to triangulate epidemiology against genetic evidence, or mentions Mendelian randomization, instrumental-variable analysis, two-sample MR, or genetic causal evidence — even if they never say "MR" (e.g. "is LDL cholesterol actually causal for heart disease?", "does BMI cause type 2 diabetes or just correlate?", "is CRP a causal driver of stroke?"). Covers trait-label resolution, MR effect direction/magnitude, instrument quality (MOE score), method agreement (IVW vs MR-Egger vs weighted median), bidirectional MR for reverse causation, and distinguishing causation from genetic correlation. Not for plain GWAS...
What it loads into context
Frontmatter
233 tokensAlways available to the agent
SKILL.md
2,649 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The three instrumental-variable assumptions
- When to use
- Anchor tools
- Workflow
- Interpretation tables
- Limitations (state these honestly)
- Reporting template
Files
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# Mendelian Randomization (Causal Inference from Genetic Instruments)
**MR estimates the CAUSAL effect of an exposure on an outcome using genetic variants as instrumental variables.** Because alleles are randomized at conception, MR is largely robust to the confounding and reverse causation that bias observational associations. It is *not* a free lunch: the causal claim rests on three assumptions, and violating them (especially horizontal pleiotropy) silently biases the estimate.
**LOOK UP, DON'T GUESS:** never assert a causal MR estimate from memory. Genetic-instrument results are updated as new GWAS are published — always retrieve current evidence with `EpiGraphDB_get_mendelian_randomization`. Do not invent beta/p-values.
**Correlation ≠ causation, and genetic correlation ≠ causation.** A high genetic correlation (`rg`) means two traits share heritability — it does NOT establish a causal direction. Only MR (with valid instruments) speaks to causality. Report them as different kinds of evidence.
## The three instrumental-variable assumptions
| Assumption | Statement | How it fails | Check |
|---|---|---|---|
| **Relevance** | Instrument is robustly associated with the exposure | Weak instruments (low F-stat) → bias toward the confounded observational estimate | MOE score; instruments selected at GWAS significance |
| **Independence** | Instrument shares no common cause with the outcome | Population stratification, assortative mating | Ancestry-matched GWAS; report population |View the complete source on GitHubtooluniverse-meta-analysis
When your agent uses it
Meta-analysis / evidence synthesis — pool effect sizes across studies (odds ratios, risk ratios, hazard ratios, mean differences, correlations, GWAS betas) with fixed- or random-effects models, quantify heterogeneity (Q, I², τ²), and build a forest plot. Use when you have results from MULTIPLE studies and need a single pooled estimate, or to synthesize evidence from a systematic review / multiple GWAS / replicated experiments. Handles the error-prone effect-size + standard-error preparation (converting OR/HR/CI, two-group means±SD, proportions, and correlations into the (effect, SE) the pooling step needs).
What it loads into context
Frontmatter
145 tokensAlways available to the agent
SKILL.md
2,120 tokensWhen the skill is activated
Reference documents
4,088 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- The workflow
- Step 1 — Convert each study to (effect_size, se) ← do this carefully
- Step 2 — Fixed vs random effects
- Step 3 — Pool with MetaAnalysis_run
- Step 4 — Interpret heterogeneity (decides the story)
- Step 5 — Forest plot + report
- Honest limitations
- Related skills
Scripts executed separately
scripts/meta_analysis.py
Files
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# Meta-Analysis & Evidence Synthesis
Pool quantitative results from **multiple studies** into one estimate, and judge how consistent the studies are. This is the statistical half of a systematic review (the literature-collection half is `tooluniverse-literature-deep-research`).
## When to use this
- You have effect sizes from ≥2 studies/cohorts and want a single pooled estimate + CI.
- Synthesizing a systematic review, replicated experiments, multi-cohort GWAS, or multi-dataset associations.
- Deciding whether studies agree (low heterogeneity) or conflict (high heterogeneity).
Do NOT use it to *find* the studies — use `tooluniverse-literature-deep-research` / the literature tools for that, then bring the extracted numbers here. Before trusting any input study, consider checking it with `Crossref_check_retraction`.
## The workflow
View the complete source on GitHubtooluniverse-metabolomics
When your agent uses it
Metabolomics research — metabolite identification, study analysis, and database searches across HMDB, MetaboLights, Metabolomics Workbench, KEGG. Use for annotating mass-spec features to known metabolites, finding metabolomics studies of a disease, and structured metabolomics research reports with metabolite-pathway mapping.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
3,043 tokensWhen the skill is activated
Reference documents
5,157 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Use Case
- Databases Covered
- Research Workflow
- Usage Patterns
- Input Parameters
- Output Format
- Implementation Notes
- Tool Discovery
- Common Issues
- Summary
- Reasoning Framework
Scripts executed separately
diabetes_analysis.pypython_implementation.pyscripts/metabolism_ref.py
Files
.env.templateasset499 bytesQUICK_START.mddoc11,961 bytesSKILL.mdcore13,232 bytesdiabetes_analysis.pyruns1,724 bytesexample1_metabolites.mddoc722 bytesexample2_study.mddoc631 bytesexample3_search.mddoc861 bytespython_implementation.pyruns10,615 bytesscripts/metabolism_ref.pyruns9,745 bytestest1_metabolites.mddoc1,011 bytestest2_study.mddoc753 bytestest3_search.mddoc859 bytestest4_comprehensive.mddoc1,663 bytes
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# Metabolomics Research
Comprehensive metabolomics research skill that identifies metabolites, analyzes studies, and searches metabolomics databases. Generates structured research reports with annotated metabolite information, study details, and database statistics.
## Use Case
**Use this skill when asked to:**
- Identify or annotate metabolites (HMDB IDs, chemical properties, pathways)
- Retrieve metabolomics study information from MetaboLights or Metabolomics Workbench
- Search for metabolomics studies by keywords or disease
- Analyze metabolite profiles or datasets
- Generate comprehensive metabolomics research reports
**Example queries:**View the complete source on GitHubtooluniverse-metabolomics-analysis
When your agent uses it
Analyze metabolomics data end-to-end — metabolite identification, quantification (TIC normalization, batch correction), differential analysis, and pathway interpretation. Use for processing mass-spec metabolomics output, normalization choice, untargeted metabolomics workflows, and integrating with other omics layers.
What it loads into context
Frontmatter
70 tokensAlways available to the agent
SKILL.md
3,426 tokensWhen the skill is activated
Reference documents
3,267 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- When to Use This Skill
- Core Capabilities
- Workflow Overview
- Phase Summaries
- Integration with ToolUniverse
- Quantified Minimums
- Limitations
- References
- Reference Files
Files
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# Metabolomics Analysis
Comprehensive analysis of metabolomics data from metabolite identification through quantification, statistical analysis, pathway interpretation, and integration with other omics layers.
## Domain Reasoning
Metabolomics quantification depends critically on normalization. Total ion current (TIC) normalization corrects for sample-loading variation and works well for global abundance changes; internal standard normalization is more accurate for targeted analysis where specific metabolite concentrations matter. Missing values in a peak table may reflect signal below the detection limit — not true absence — and should be imputed or handled explicitly rather than treated as zero. Failing to account for batch effects across instrument runs is a frequent source of spurious differential metabolites.
## LOOK UP DON'T GUESS
- Metabolite identities: use `Metabolite_search` and `Metabolite_get_info` to confirm names, CIDs, and HMDB IDs; never assume identity from m/z alone.
- Pathway memberships: query KEGG, MetaCyc, or Reactome tools; do not list pathways from memory.
- Disease associations: retrieve from CTD via `Metabolite_get_diseases`; do not infer clinical relevance without database evidence.
- CV thresholds and QC criteria: apply the values defined in this workflow (CV < 30%, blank ratio > 3x); do not override with guesses.View the complete source on GitHubtooluniverse-metabolomics-pathway
When your agent uses it
Metabolomics pathway analysis — metabolite identification (HMDB, KEGG, ChEBI), pathway mapping (Reactome, KEGG, MetaCyc), disease associations, enzyme/gene linkage. Use for metabolite-to-pathway-to-disease connections, BridgeDb-based ID conversion, and integrating metabolomics with gene-level pathway analyses.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
1,947 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Workflow
- Phase 0: Metabolite Identification & Resolution
- Phase 1: Metabolite Characterization
- Phase 2: Pathway Mapping
- Phase 3: Enzyme & Gene Linkage
- Phase 4: Disease Associations
- Phase 5: Cross-Database Enrichment
- Common Mistakes to Avoid
- Fallback Strategies
- Evidence Grading
- Limitations
Files
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# Metabolomics Pathway Analysis
Identify metabolites, map to metabolic pathways, find disease associations, and connect to enzymes/genes.
## Domain Reasoning
Metabolite-to-pathway mapping requires correct, database-specific identifiers. HMDB IDs link to KEGG/Reactome but must be converted via BridgeDb; PubChem CIDs need explicit cross-referencing. Always verify metabolite identity first: the same common name can refer to structurally distinct isomers, and PubChem names frequently differ from CTD/KEGG names.
## LOOK UP DON'T GUESS
- Pathway membership: call `MetaCyc_get_compound`, `KEGG_get_compound`, or `ReactomeContent_search`
- Cross-database IDs: use `BridgeDb_xrefs`
- Enzyme-metabolite relationships: use `CTD_get_chemical_gene_interactions` or `KEGG_get_compound`
- Disease associations: query `Metabolite_get_diseases` or `CTD_get_chemical_diseases`View the complete source on GitHubtooluniverse-metagenomics-analysis
When your agent uses it
Microbiome and metagenomics analysis using MGnify, GTDB taxonomy, ENA sequencing data, and EuropePMC literature. Covers taxonomic classification, genome quality assessment, biome-clinical phenotype linkage, and pathway interpretation. Use for amplicon/shotgun metagenomics study analysis.
What it loads into context
Frontmatter
75 tokensAlways available to the agent
SKILL.md
1,267 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Core Databases
- Workflow
- Key Phase Notes
- Edge Cases & Fallbacks
- Limitations
Files
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# Metagenomics & Microbiome Analysis
Integrated pipeline for exploring microbiome studies, classifying taxa, assessing genome quality, linking microbial composition to clinical phenotypes, and interpreting findings through pathway analysis and literature context.
**Guiding principles**:
1. **Study context first** -- understand biome, sequencing method, and metadata before diving into taxa
2. **Taxonomic consistency** -- GTDB taxonomy as reference standard; reconcile NCBI where needed
3. **Genome quality matters** -- CheckM completeness/contamination thresholds determine trustworthy MAGs
4. **Interpretation over enumeration** -- explain what taxa mean for the biological question
5. **English-first queries** -- use English terms in tool calls
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.
View the complete source on GitHubtooluniverse-microbial-genome-characterization
When your agent uses it
Genome-ASSEMBLY discovery, QC, and replicon mapping for any organism (bacteria, archaea, fungi, and beyond) using NCBI Datasets. Resolves an organism name or taxid to assemblies, picks the reference/representative or best-quality assembly, pulls assembly QC metrics (total length, contig/scaffold N50, contig count, GC%, assembly level, RefSeq category), enumerates chromosomes and plasmids via per-replicon sequence reports, and compares candidate assemblies on quality. Use for "what genomes are available for [organism]", "assembly stats / N50 / GC content for [GCF_/GCA_ accession]", "how many plasmids does [strain] have", "compare assemblies for [species]", "find the reference genome for [taxon]", "is this assembly Complete Genome or just contigs". NOT for gene-level orthology/synteny (use tooluniverse-comparative-genomics), plant gene structure (use tooluniverse-plant-genomics), de novo assembly from raw reads (no tool exists), or taxonomy-only name/lineage lookups.
What it loads into context
Frontmatter
250 tokensAlways available to the agent
SKILL.md
3,404 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Tools (all keyless, verified live)
- Workflow
- Interpretation Table
- Worked Example 1 — Reference genome of *E. coli* (single chromosome, no plasmid)
- Worked Example 2 — "How many plasmids does *E. coli* O157:H7 Sakai have?"
- Worked Example 3 — Inventory scale (large taxon)
- Limitations
- Completeness Checklist
Files
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# Microbial Genome Assembly Characterization & QC
Discover, quality-control, and structurally map genome ASSEMBLIES for any organism using the keyless NCBI Datasets genome tools. Organism/taxon in → assembly inventory, QC metrics, and chromosome/plasmid map out.
## LOOK UP, DON'T GUESS
When uncertain about an accession, assembly level, replicon count, or N50, CALL the tool. Never report assembly statistics from memory — accessions and metrics change with each RefSeq release. A live NCBI Datasets answer is always more reliable than a guess.
## COMPUTE, DON'T DESCRIBE
When comparing multiple assemblies or ranking by quality, retrieve each via the tools, then write and run Python (pandas) over the returned JSON to sort, score, and tabulate. Don't describe what you would compute — execute it and report actual numbers.
## When to Use This Skill
**Triggers**:
- "What genomes are available for [organism]?" / "Find the reference genome for [taxon]"View the complete source on GitHubtooluniverse-microbiome-research
When your agent uses it
Microbiome research using MGnify, GTDB, ENA, OLS (ENVO biomes), EuropePMC, BacDive (strain phenotypes), MediaDive (growth media recipes), and GMrepo (gut microbiome disease-association counts). Covers study discovery, taxonomic profiling, host-microbe interaction analysis, biome-by-condition queries, strain culturing requirements, and phenotype/disease prevalence lookups. Use for microbiome study selection, organism-environment associations, clinical-microbiome literature review, and "how do I grow this organism" / "how prevalent is this species in condition X" questions. Distinct from analytical workflow (use tooluniverse-metagenomics-analysis for that).
What it loads into context
Frontmatter
160 tokensAlways available to the agent
SKILL.md
4,229 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core Tools
- Quick Start
- Common Workflows
- MGnify Biome Hierarchy
- Key Identifiers
- Reasoning Framework
- Tips
Files
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# Microbiome Research with ToolUniverse
Comprehensive microbiome analysis using MGnify (EBI metagenomics), GTDB (genome taxonomy), ENA (sequencing data), OLS (ontology lookup for ENVO biomes), and EuropePMC (literature).
## Core Tools
| Tool | Purpose | Auth |
|------|---------|------|
| **MGnify_search_studies** | Find metagenomics studies by biome/keyword | None |
| **MGnify_get_study_detail** | Study metadata, abstract, sample counts | None |
| **MGnify_list_analyses** | List taxonomic/functional analysis outputs for a study | None |
| **MGnify_get_taxonomy** | Taxonomic composition from an analysis | None |
| **MGnify_get_go_terms** | GO functional annotations from an analysis | None |
| **MGnify_get_interpro** | InterPro protein domain annotations | None |View the complete source on GitHubtooluniverse-ml-inference-utility
When your agent uses it
Generic ML/NLP/vision inference against any HuggingFace-hosted model (serverless hf-inference provider) — text classification/sentiment, dense text embeddings for semantic similarity, masked-token prediction (including protein language models), summarization, zero-shot classification against caller-supplied labels, named entity recognition, extractive question answering, translation, image classification, and object detection. Use when someone asks to "classify this text with a HuggingFace model", "embed this text for similarity search", "run NER on this passage", "summarize this with model X", "zero-shot classify this against these labels", "predict the masked token", "classify/detect objects in this image", or names a specific HuggingFace model_id to run inference with. NOT a domain-analysis skill itself — this is a generic cross-domain utility other skills should reach for as a supporting step, the same way tooluniverse-clinical-terminology-lookup is a supporting lookup step rather...
What it loads into context
Frontmatter
219 tokensAlways available to the agent
SKILL.md
3,707 tokensWhen the skill is activated
Reference documents
1,880 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first) — the token requirement is not actually optional
- When to Use This Skill
- The 10 Tools
- Choosing a `model_id`
- Workflow
- Finding a model first: HuggingFace Hub search (a different API, no token needed)
- An alternative inference platform: Replicate
- Reference
Files
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# Generic ML/NLP/Vision Inference (HuggingFace)
Run any HuggingFace-hosted model for a standard inference task via ToolUniverse's
10 `HFInference_*` tools. This is a **utility skill**: reach for it as a supporting
step inside another workflow (e.g. summarizing a batch of retrieved abstracts,
extracting entities from clinical text, embedding sentences for a similarity
search) rather than treating it as a research domain in its own right.
## Honesty contract (read first) — the token requirement is not actually optional
Every tool's own JSON description says `HF_TOKEN` is an *optional* API key that
merely "raises rate limits." **Live testing on this date contradicts that**: all
10 tools, tested against the exact model IDs in their own `test_examples`, fail
with the identical error with no token set:View the complete source on GitHubtooluniverse-model-organism-genetics
When your agent uses it
Cross-species genetic analysis using model organism databases (MGI mouse, IMPC systematic knockout phenotyping, ZFIN zebrafish, FlyBase fruit fly, WormBase worm, SGD budding yeast plus protein-domain/PTM/literature detail, PomBase fission yeast, RGD rat, GeneNetwork systems-genetics/eQTL panels, HumanMine/MouseMine cross-species data-warehouse search, GBIF taxonomy) plus VEuPathDB for eukaryotic pathogens (Plasmodium, Toxoplasma, fungi, vectors). Maps human genes to orthologs, retrieves phenotype/expression/functional data, assesses gene function conservation, and identifies the best animal model for studying a human gene or disease.
What it loads into context
Frontmatter
162 tokensAlways available to the agent
SKILL.md
8,980 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- Reasoning Principles
- Pipeline
- Bacterial and Classical Genetics Reasoning
- Completeness Checklist
Files
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Model Organism Genetics Pipeline
Map human genes to model organism orthologs and retrieve phenotype, expression, and functional data across six species. Synthesize cross-species evidence to assess gene function conservation and identify the best animal models for studying human genes and diseases.
**Not for**: human variant interpretation (`tooluniverse-variant-analysis`), drug target validation (`tooluniverse-drug-target-validation`), human disease characterization (`tooluniverse-multiomic-disease-characterization`), pathogen outbreak/drug-repurposing intelligence once a VEuPathDB target gene is identified (`tooluniverse-infectious-disease`).
**LOOK UP, DON'T GUESS**: When asked about a species' taxonomy, ecology, or biology, search GBIF/NCBI Taxonomy first. For GBIF: use `GBIF_search_species(query="species name")`, then use the `nubKey` (not `key`) from the result to call `GBIF_get_species(speciesKey=nubKey)` for full taxonomy (kingdom, phylum, class, order, family). The `nubKey` is the GBIF backbone key; the `key` is dataset-specific and often lacks higher taxonomy.
---
## Reasoning PrinciplesView the complete source on GitHubtooluniverse-molecular-cloning
When your agent uses it
Molecular cloning, in both directions. DESIGN — Gibson Assembly (overlap design for seamless multi-fragment joining) and Golden Gate Assembly (Type IIS / BsaI / BbsI / Esp3I / BsmBI / SapI design with unique 4-bp fusion overhangs). ANALYSIS — work out what an existing reaction produces: given input plasmid sequences and an enzyme, digest them, join the fragments by their overhangs, and identify features of the product (expressed ORF, gRNA spacer and its target gene). Use when you need to plan how to join DNA fragments into a construct, design assembly overlaps/overhangs, decide between cloning methods, or determine the product of a stated Gibson/Golden Gate reaction. Covers the domestication (internal-site removal), overhang-uniqueness, and overlap-Tm rules. For PCR primers to generate the fragments, see tooluniverse-primer-design.
What it loads into context
Frontmatter
206 tokensAlways available to the agent
SKILL.md
1,936 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Step 0 — Pick the method
- Step 1 — Gibson Assembly
- Step 2 — Golden Gate Assembly
- Step 3 — QC before ordering
- Step 4 — Gotchas (state these)
- Working backwards — what does an existing assembly produce?
- Honest limitations
- Related skills
Scripts executed separately
scripts/cloning_qc.py
Files
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# Molecular Cloning Assembly Design (Gibson & Golden Gate)
Plan how to join DNA fragments into a construct: design the **overlaps** (Gibson) or **Type IIS overhangs** (Golden Gate) and avoid the failures that come from internal sites and non-unique junctions.
## Step 0 — Pick the method
| Use **Gibson Assembly** when | Use **Golden Gate** when |
|---|---|
| A few fragments, **scarless/seamless** junctions anywhere you choose | Many parts, **standardized reusable** parts (MoClo/modular), one-pot |
| You can add ~20–40 bp homology by PCR | You can remove internal BsaI/BbsI sites (domestication) |
| One-off constructs | Combinatorial libraries / repeated assemblies |
Both are sequence-independent (no scar at the junction for Gibson; a 4-bp fusion scar for Golden Gate). For 2–4 unique fragments, Gibson is usually simplest; for libraries or a parts toolkit, Golden Gate.
View the complete source on GitHubtooluniverse-multi-omics-integration
When your agent uses it
Multi-omics integration — orchestrate per-layer analysis (transcriptomics, proteomics, epigenomics, genomics, metabolomics) then perform cross-omics correlation, multi-omics clustering, and pathway-level integration. Use for integrative systems-biology analysis, multi-modal disease characterization, and cross-omics biomarker discovery.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
1,793 tokensWhen the skill is activated
Reference documents
2,357 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- When to Use This Skill
- Workflow Overview
- Supported Data Types
- Core Operations
- Multi-Omics Clustering Methods
- ToolUniverse Skills Coordination
- Use Cases
- Quantified Minimums
- Limitations
- References
- Detailed Reference
Files
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# Multi-Omics Integration
Coordinate and integrate multiple omics datasets for comprehensive systems biology analysis. Orchestrates specialized ToolUniverse skills to perform cross-omics correlation, multi-omics clustering, pathway-level integration, and unified interpretation.
---
## Domain Reasoning
Multi-omics integration asks whether different molecular layers tell a concordant story. If a gene is upregulated in RNA-seq AND its protein is elevated in proteomics, that is concordant evidence of true biological change. Discordance — high mRNA but low protein, or elevated protein without matching mRNA — may indicate post-transcriptional regulation (miRNA silencing, protein degradation, translational control) and is itself a meaningful finding worth reporting. Not every discordance is noise; some are the most interesting biology.
## LOOK UP DON'T GUESS
- Expected RNA-protein correlation ranges: compute Spearman r from the actual data; the typical range (0.4-0.6) is a guide, not a guarantee.
- Pathway enrichment results: run `ReactomeAnalysis_pathway_enrichment` or gseapy on the actual gene lists; never list enriched pathways from memory.View the complete source on GitHubtooluniverse-multiomic-disease-characterization
When your agent uses it
Comprehensive disease characterization across genomics, transcriptomics, proteomics, and pathways for systems-level understanding. Identifies therapeutic opportunities and biomarker candidates by integrating multi-layer molecular data. Use for full-omics disease deep-dive reports, mechanism mapping, and biomarker-and-target identification from multi-omics data.
What it loads into context
Frontmatter
79 tokensAlways available to the agent
SKILL.md
2,005 tokensWhen the skill is activated
Reference documents
9,265 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use This Skill
- Input Parameters
- Pipeline Overview
- Key Tool Parameter Notes
- Reference Files
Files
.env.templateasset499 bytesSKILL.mdcore8,883 bytesintegration-scoring.mddoc4,491 bytesreport-template.mddoc7,546 bytesresponse-formats.mddoc2,415 bytestool-reference.mddoc16,089 bytesuse-patterns.mddoc3,592 bytes
Read SKILL.md
# Multi-Omics Disease Characterization Pipeline
Characterize diseases across multiple molecular layers (genomics, transcriptomics, proteomics, pathways) to provide systems-level understanding of disease mechanisms, identify therapeutic opportunities, and discover biomarker candidates.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, then populate progressively
2. **Disease disambiguation FIRST** - Resolve all identifiers before omics analysis
3. **Layer-by-layer analysis** - Systematically cover all omics layers
4. **Cross-layer integration** - Identify genes/targets appearing in multiple layers
5. **Evidence grading** - Grade all evidence as T1 (human/clinical) to T4 (computational)
6. **Tissue context** - Emphasize disease-relevant tissues/organs
7. **Quantitative scoring** - Multi-Omics Confidence Score (0-100)
8. **Druggable focus** - Prioritize targets with therapeutic potential
9. **Biomarker identification** - Highlight diagnostic/prognostic markersView the complete source on GitHubtooluniverse-natural-product-dereplication
When your agent uses it
Dereplicate a putative natural product and assign its chemical taxonomy. Use to answer "is [compound] a known natural product", "what microbe/organism produces [compound]", "what chemical class is [compound]", "dereplicate this metabolite (by formula/exact mass/InChIKey/SMILES)", or "classify this molecule into ChemOnt". Searches NPAtlas for known microbial natural products (producing organism + literature reference), assigns the ChemOnt kingdom→superclass→class→subclass hierarchy via ClassyFire, resolves systematic IUPAC names to structure via OPSIN, and cross-references identity in PubChem. NOT for general drug/compound identity or ADMET (use tooluniverse-chemical-compound-retrieval / tooluniverse-small-molecule-discovery) and NOT for metabolomics pathway/enrichment analysis (use tooluniverse-metabolomics skills).
What it loads into context
Frontmatter
204 tokensAlways available to the agent
SKILL.md
3,954 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Backing Tools (all keyless; verify before quoting)
- Workflow
- Interpretation Guidance (dereplication logic)
- Worked Example — Staurosporine (trivial name in)
- Worked Example — Acetylsalicylic acid (systematic name in)
- Limitations (be honest in every report)
- Out of Scope → route elsewhere
- Completeness Checklist
Files
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Read SKILL.md
# Natural Product Dereplication & Chemotaxonomy
Decide whether a putative natural product is **already known**, identify the **microbe that produces it**, attach the **literature reference**, and assign its **ChemOnt chemical class**. This is the dereplication question every NP chemist and metabolomics analyst asks of a new feature: *"have we seen this before, and what makes it?"*
**LOOK UP DON'T GUESS**: Never assume an NPAID, producing organism, exact mass, or chemical class. Every identity, provenance, and taxonomy claim must come from a live tool call.
**Scope (microbial NPs only)**: NPAtlas covers natural products from **bacteria and fungi**. It does NOT cover plant, animal, or marine-invertebrate metabolites unless a microbial producer was reported. A "no NPAtlas hit" therefore means *not a known microbial NP* — it does not prove the molecule is novel in an absolute sense.
---
## Backing Tools (all keyless; verify before quoting)
| Tool | Input | Returns |
|------|-------|---------|View the complete source on GitHubtooluniverse-network-pharmacology
When your agent uses it
Compound-target-disease network construction and analysis for drug repurposing, polypharmacology discovery, and multi-target drug design. Uses STRING, BioGRID, ChEMBL, DGIdb, OMIM, OpenTargets. Use for off-target effect prediction, network-based drug repurposing, and identifying molecules with desired multi-target profile.
What it loads into context
Frontmatter
83 tokensAlways available to the agent
SKILL.md
3,315 tokensWhen the skill is activated
Reference documents
37,363 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- Polypharmacology Reasoning (Start Here)
- When to Use This Skill
- Key Principles
- Network Pharmacology Score (0-100)
- Workflow Overview
- Critical Tool Parameter Notes
- Fallback Strategies
- Reference Files
- Related Skills
Scripts executed separately
scripts/network_proximity.py
Files
../tooluniverse-adverse-event-detection/SKILL.mddoc12,440 bytes../tooluniverse-drug-repurposing/SKILL.mddoc19,811 bytes../tooluniverse-drug-target-validation/SKILL.mddoc19,387 bytesANALYSIS_PROCEDURES.mddoc16,052 bytesQUICK_START.mddoc12,425 bytesREPORT_TEMPLATE.mddoc5,115 bytesSCORING_REFERENCE.mddoc3,037 bytesSKILL.mdcore14,171 bytesSKILL.mdcore14,171 bytesSKILL.mdcore14,171 bytesTOOL_REFERENCE.mddoc10,629 bytesUSE_PATTERNS.mddoc3,469 bytesscripts/network_proximity.pyruns6,310 bytes../tooluniverse-protein-interactions/SKILL.mddoc17,244 bytes../tooluniverse-systems-biology/SKILL.mddoc27,288 bytes
Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Network Pharmacology Pipeline
Construct and analyze compound-target-disease (C-T-D) networks to identify drug repurposing opportunities, understand polypharmacology, and predict drug mechanisms using systems pharmacology approaches.
**LOOK UP DON'T GUESS** - Retrieve actual target lists, network data, and clinical evidence from tools. Do not infer network relationships from drug class alone.
**IMPORTANT**: Always use English terms in tool calls, even if the user writes in another language. Respond in the user's language.
---
## Polypharmacology Reasoning (Start Here)View the complete source on GitHubtooluniverse-neuroscience
When your agent uses it
Neuroscience research workflows: neuroanatomy, neural circuits, neurotransmitter biology, neurological/psychiatric disease genetics, neural-protein function. Uses Allen Brain Atlas, WormBase (C. elegans connectome), UniProt for neural proteins, PubMed for primary literature. Use for brain-region biology, neural development, neurodegeneration mechanisms (Alzheimer's, Parkinson's, ALS), and synaptic-protein characterization.
What it loads into context
Frontmatter
101 tokensAlways available to the agent
SKILL.md
6,811 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- 1. Computational Neuroscience Reasoning
- 2. Neuroanatomy Reasoning
- 3. Clinical Neurology Reasoning
- 4. Neurophysiology Reasoning
- 5. Available Tools
- 6. C. elegans Connectome Lookups
- 7. Single-Neuron Morphology Lookups (NeuroMorpho)
- 8. Neuroimaging Dataset Discovery (OpenNeuro)
- 9. Computational Model Lookups (ModelDB)
- 10. Neuroimaging Statistical Map Discovery (NeuroVault)
- 11. Choosing Among the Four Neuroscience Data Resources
- 12. Common Pitfalls
Files
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# Neuroscience Research Skill
**KEY PRINCIPLES**: LOOK UP, DON'T GUESS — use PubMed/EuropePMC for neuroanatomy facts, WormBase for C. elegans connectome data, UniProt for neural protein properties. Verify claims with literature before answering. Use Python computation for quantitative neuroscience problems.
---
## LOOK UP, DON'T GUESS
When uncertain about any neuroscience fact — brain region function, neural circuit connectivity, ion channel properties, neurotransmitter receptor subtypes — SEARCH databases first. A PubMed-verified answer is always more reliable than reasoning from memory. This is especially critical for neuroanatomy, where structures have precise boundaries and connectivity patterns that are easy to confuse.
---
## 1. Computational Neuroscience Reasoning
### Rate-Based ModelsView the complete source on GitHubtooluniverse-neurosynth-meta-analysis
When your agent uses it
Meta-analytic decoding of human fMRI coordinates using the Neurosynth coordinate database (14,371 studies, ~3,228 cognitive/neuroscience terms) — given an MNI brain coordinate, statistically identify which cognitive terms are over-represented in studies reporting activation there ("reverse inference"); given a term, find the studies and coordinates most associated with it ("forward" lookup). Use when someone asks "what does this brain coordinate/region do", "decode this fMRI activation peak", "what terms are associated with [MNI coordinate]", "find studies about [cognitive term] and their activation coordinates", or "reverse-infer cognitive function from a brain location". This is real local analysis on a downloaded, cached coordinate database — not a live API — following ToolUniverse's honesty-contract pattern (real computed statistics, no fabricated term associations).
What it loads into context
Frontmatter
190 tokensAlways available to the agent
SKILL.md
1,784 tokensWhen the skill is activated
Reference documents
1,343 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to Use This Skill
- Setup
- Workflow
- Reference
Scripts executed separately
scripts/_neurosynth_data.pyscripts/decode_coordinate.pyscripts/download_data.pyscripts/search_by_term.py
Files
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Read SKILL.md
# Neurosynth Meta-Analytic Coordinate Decoding
Statistically decode human fMRI brain coordinates against ~14,000 published neuroimaging studies, or look up which studies/coordinates are associated with a cognitive term — using the real, freely-downloadable Neurosynth coordinate database (no API key, no login).
## Honesty contract (read first)
This skill downloads a real coordinate database and runs real statistics on it. It must never fabricate a term association, a study result, or a p-value.
1. **Preflight before anything.** Run `scripts/download_data.py` first. If any file fails to download, STOP and report the failure — do not proceed with partial data or describe hypothetical decoding results.
2. **Never guess a term.** `search_by_term.py` requires an exact (case-sensitive) vocabulary match. If the term isn't found, it returns suggestions (substring matches, or close spellings when there are none, e.g. `hipocampus` → `hippocampus`) — use those, don't invent a plausible-sounding term.
3. **Report the multiple-testing caveat every time.** `decode_coordinate.py` tests ~3,228 terms simultaneously per query. Uncorrected p-values will produce false positives at this scale — always surface the FDR-corrected `fdr_q_value`, not just the raw `p_value`, when characterizing how confident a result is.
4. **This is a simplified reverse-inference test, not a full meta-analysis pipeline.** It does binary term-presence comparison (near-coordinate studies vs. rest of corpus) via a two-proportion z-test — a real, defensible statistical test, but not the same as running NiMARE's full activation-likelihood-estimation (ALE) or multilevel kernel density analysis (MKDA) methods. Say so if precision matters (e.g., the user is drafting something for publication).
5. **Never overwrite the cached data files** — `download_data.py` writes to a dedicated cache directory and is idempotent (skips re-download if already cached, unless `--force`).
View the complete source on GitHubtooluniverse-nfcore-pipelines
When your agent uses it
Run real end-to-end bioinformatics pipelines (nf-core rnaseq, sarek, atacseq) via Nextflow + Docker/Singularity, for raw FASTQ/BAM data — either local files or public GEO/SRA datasets. Use when someone asks to "run RNA-seq alignment", "quantify gene expression from FASTQs", "call germline or somatic variants from my WGS/WES data", "run an nf-core pipeline", "analyze ATAC-seq chromatin accessibility data", "reanalyze a GEO/SRA dataset (GSE/GSM/SRR accession)", "generate a samplesheet for nf-core", or "set up Nextflow to process my sequencing data". Orchestrates the full pipeline (alignment/quantification, or variant calling, or peak calling) rather than a single step. Honest: shells out to real Nextflow/Docker; checks the environment first and stops with install instructions rather than fabricating pipeline output; always runs the pipeline's small test profile before real data.
What it loads into context
Frontmatter
223 tokensAlways available to the agent
SKILL.md
3,132 tokensWhen the skill is activated
Reference documents
3,228 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Honesty contract (read first)
- When to use vs. not
- Requirements (be upfront about these)
- Workflow Checklist
- Step 0: Acquire Data (GEO/SRA only)
- Step 1: Environment Check
- Step 2: Select Pipeline
- Step 3: Run Test Profile
- Step 4: Generate Samplesheet
- Step 5: Configure & Run
- Step 6: Verify Outputs
- References
- Disclaimer
Scripts executed separately
scripts/check_environment.pyscripts/detect_data_type.pyscripts/generate_samplesheet.py
Files
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Read SKILL.md
# nf-core Pipeline Orchestration
Run real nf-core bioinformatics pipelines end-to-end for bench scientists who
need alignment/quantification, variant calling, or chromatin-accessibility
analysis without hand-building a pipeline.
## Honesty contract (read first)
This skill drives **real Nextflow pipelines** on **real compute**. It must
never fabricate a pipeline result.
1. **Preflight before anything.** Run `scripts/check_environment.py`. If
Docker, Nextflow (>=23.04), or Java (>=11) is missing, print the install
plan and STOP. Do not describe hypothetical alignment/variant-callingView the complete source on GitHubtooluniverse-nih-funding-landscape
When your agent uses it
Analyze NIH grant portfolios and funding history using the OpenNIH FY1985-present corpus, then connect grants to investigators, institutions, publications, clinical trials, patents, targets, or drugs with ToolUniverse. Use for NIH grant discovery, topic or Institute/Center trends, PI and institution profiles, activity-code or mechanism analysis, funding growth and concentration, grant-writing landscape research, SBIR/STTR landscapes, research-policy analysis, expert discovery, funding-to-output translational impact studies, or public-facing NIH explainers for patients, advocates, local and national journalists, trainees, applicants, institutions, entrepreneurs, and taxpayers.
What it loads into context
Frontmatter
139 tokensAlways available to the agent
SKILL.md
5,663 tokensWhen the skill is activated
Reference documents
18,351 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Public Value Routing
- Required Workflow
- Endpoint-Specific Interpretation
- Analysis Rules
- Quantified Completeness
- Cross-Source Linking
- Output
- Quality Check
Scripts executed separately
scripts/verify_live_cases.pyscripts/verify_live_contract.pyscripts/verify_output_links.py
Files
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Read SKILL.md
# NIH Funding Landscape
Build source-traceable NIH funding analyses with explicit scopes, count units, data coverage, and interpretation limits. Use `OpenNIH_*` for funding facts and other ToolUniverse sources only for downstream evidence they actually cover.
Always read [references/tool-reference.md](references/tool-reference.md) before choosing tools or comparing totals. Read [references/verified-cases.md](references/verified-cases.md) when testing the skill, debugging an unexpected response, or adapting one of the verified case patterns. Read [references/public-value-cases.md](references/public-value-cases.md) for a patient, family, advocate, journalist, trainee, applicant, taxpayer, policy, or other public-facing request.
## Public Value Routing
Start from the reader's decision, not from the available endpoints:
| Reader | Optimize the answer for | Never imply |
|---|---|---|
| Patient, family, advocate | A topic-specific research map, recent activity, inspectable projects, and next contacts | Clinical expertise, quality of care, treatment advice, or patient benefit from funding alone |
| Journalist, taxpayer, policy analyst | A reproducible headline number, its definition, its largest drivers, and its sensitivity to alternate queries | That the largest number is the truest, a partial year is final, or spending caused outcomes |View the complete source on GitHubtooluniverse-noncoding-rna
When your agent uses it
Non-coding RNA analysis — miRNAs (miRBase, miRDB targets), lncRNAs (LNCipedia, RNAcentral), circRNAs, snoRNAs, and other ncRNA classes. Distinct mechanisms per class — miRNAs repress mRNA; lncRNAs scaffold/decoy/enhance. Use for ncRNA function prediction, miRNA-target prediction, lncRNA functional annotation, and ncRNA-disease association queries.
What it loads into context
Frontmatter
106 tokensAlways available to the agent
SKILL.md
4,301 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- Core Tools
- Workflow
- Limitations
- Limitations
Files
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# Non-Coding RNA Analysis
Pipeline for identifying, annotating, and interpreting non-coding RNAs and their biological roles. Covers microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and other ncRNA classes.
**Key principles**:
1. **Class determines function** — miRNAs repress mRNA translation; lncRNAs have diverse mechanisms (scaffolds, guides, decoys, enhancers); rRNAs/tRNAs are structural
2. **Targets matter more than the ncRNA itself** — for miRNAs, the regulated mRNA targets determine the phenotype
3. **Expression context is critical** — ncRNAs are highly tissue/cell-type specific
4. **Conservation indicates function** — deeply conserved ncRNAs (miR-let-7, MALAT1) have well-established roles
5. **Evidence grading** — T1: validated targets (reporter assay, CLIP-seq), T2: high-confidence computational prediction, T3: expression correlation, T4: sequence-based prediction only
**Type-based reasoning — look up, don't guess**:
Non-coding RNA function depends on type: miRNA silences target mRNAs (look up targets in miRTarBase/TargetScan), lncRNA has diverse functions (scaffolding, guiding, decoying — check literature for the specific lncRNA), circRNA may sponge miRNAs.
View the complete source on GitHubtooluniverse-nutrition-food-composition
When your agent uses it
Look up nutrient composition, ingredients, allergens, additives, and processing-level classification for foods and branded products via USDA FoodData Central (authoritative US nutrient values, generic + branded foods) and Open Food Facts (crowdsourced global product database — barcode lookup, allergens, additives, Nutri-Score, NOVA processing classification). Use when someone asks 'how much [nutrient] is in [food]', 'what are the ingredients/allergens in [branded product]', 'look up this barcode', 'find products containing/free of [additive/allergen]', or needs dietary/nutrient data for a clinical or research calculation.
What it loads into context
Frontmatter
144 tokensAlways available to the agent
SKILL.md
1,922 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- 1. USDA FoodData Central — Authoritative US Nutrient Values
- 2. Open Food Facts — Crowdsourced Global Product Database
- When to Use Which
- Common Pitfalls
- References
Files
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# Nutrition & Food Composition Lookup
Looks up what's actually in a food or branded product — nutrient values, ingredients, allergens, additives, processing classification — from two complementary sources. Does not itself perform toxicological or epidemiological analysis; feed the retrieved data into `tooluniverse-toxicology` (food additive/allergen safety questions) or `tooluniverse-epidemiological-analysis` (dietary-exposure questions) for that.
**LOOK UP, DON'T GUESS**: Never state a nutrient value, ingredient list, allergen, or Nutri-Score from memory — both sources are live and current; query them.
## When to Use This Skill
- "How much [protein/sodium/vitamin C/...] is in [food]?" -> FoodDataCentral
- "What's the full nutrient profile of [generic or USDA reference food]?" -> FoodDataCentral
- "What are the ingredients/allergens in [branded product]?" -> OpenFoodFacts
- "Look up this barcode" -> `OpenFoodFacts_get_product`
- "Find products with/without [additive, allergen, Nutri-Score grade, label]" -> `OpenFoodFacts_filter_products_by_tags`
View the complete source on GitHubtooluniverse-organic-chemistry
When your agent uses it
Organic chemistry reasoning guide for reaction product prediction, mechanism analysis (electrophilic/nucleophilic substitution, addition, elimination, pericyclic, radical), and spectroscopy interpretation (1H/13C NMR, IR, MS). Reasons from first principles (electron flow, kinetic vs thermodynamic) rather than pattern-matching named reactions. Use for organic synthesis problems and mechanism explanations.
What it loads into context
Frontmatter
92 tokensAlways available to the agent
SKILL.md
5,756 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Predicting Reaction Products — A Reasoning Process
- 2. Spectroscopy Interpretation — A Problem-Solving Strategy
- 3. Stereochemistry — How to Think in 3D
- 4. Bundled Scripts
- 5. Computational Procedures
- 6. When to Reason vs When to Look Things Up
Scripts executed separately
scripts/chemistry_facts.pyscripts/crystal_validator.pyscripts/degrees_of_unsaturation.pyscripts/molecular_complexity.pyscripts/molecular_formula.pyscripts/smiles_verifier.pyscripts/stereochem_tracker.py
Files
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Read SKILL.md
# Organic Chemistry Reasoning Guide
This skill teaches **how to think through** organic chemistry problems, not what to memorize.
---
## 1. Predicting Reaction Products — A Reasoning Process
Do NOT pattern-match to a named reaction. Instead, reason from first principles:
### Step 1 — Find the most reactive sites
Look at each molecule and ask: where is the electron density?
- **Electron-rich sites** (nucleophiles): lone pairs on N, O, S; pi bonds (C=C, aromatics); carbanions; enolatesView the complete source on GitHubtooluniverse-pathway-disease-genetics
When your agent uses it
Connect GWAS variants to biological pathways and druggable targets. Maps GWAS hits to causal genes (via fine-mapping/eQTL), then to pathways (Reactome, KEGG, WikiPathways), then to existing drugs hitting those pathways. Use for pathway-level disease mechanisms, druggable-pathway prioritization from GWAS, SNP-to-pathway-to-target tracing, and tissue-specific eQTL evidence for drug target hypotheses.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
2,468 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Core Reasoning Principles
- Tool Selection Guide
- Three-Step Workflow
- Key Parameter Gotchas
- Limitations
- Related Skills
Files
SKILL.mdcore10,062 bytes
Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Pathway-Disease Genetics: GWAS to Drug Targets via Pathways
Connect genome-wide association study (GWAS) variants to biological pathways for mechanistic understanding and drug target discovery.
## When to Use
- "What pathways are disrupted in [disease] based on GWAS?"
- "Which GWAS genes for [trait] are in druggable pathways?"
- "Map [SNP/variant] to its causal gene and pathway"
- "Find drug targets from GWAS data for [disease]"
- "What is the eQTL evidence for [gene] in [tissue]?"View the complete source on GitHubtooluniverse-peptide-target-deorphanization
When your agent uses it
Find the real protein target(s) of a peptide from its sequence — peptide target deorphanization / off-target identification, for ANY target class (GPCR, ion channel, protease, cytokine/growth-factor receptor, enzyme, integrin), not only GPCRs. Use when a peptide has a phenotype but does not bind its hypothesized target, when a peptide binds a target in one species or assay but not another, or to screen candidate targets for an orphan peptide. A target-class router steers a multi-route keyless pipeline (PROSITE/ELM motif, BLAST homology, HGNC/InterPro/GPCRdb/GtoPdb target-family enumeration, OpenTargets phenotype anchor, EnsemblCompara/Alliance cross-species reconciliation) plus optional NVIDIA-NIM co-folding (Boltz2, AlphaFold2-Multimer, OpenFold3) for structural confirmation.
What it loads into context
Frontmatter
201 tokensAlways available to the agent
SKILL.md
4,513 tokensWhen the skill is activated
Reference documents
11,637 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Core reasoning: LOOK UP, DON'T GUESS
- Automated pipeline (scripts) — the fast path
- The pipeline at a glance
- Output format — ranked shortlist report (return inline, no extra files)
- Peptide Bioactivity Property Databases (supplementary characterization)
- Validation & test set
Scripts executed separately
scripts/cofold_screen.pyscripts/deorphanize_peptide.py
Files
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Read SKILL.md
# Peptide Target Deorphanization
Deorphanize a peptide: given a peptide **sequence** plus an **observed phenotype** (and often a *hypothesized* target the peptide does NOT actually bind), find its likely real protein target(s) — using ToolUniverse's keyless characterization, homology, target-family, phenotype, cross-species, and (optional, key-gated) co-folding tools.
**The target can be anything, not just a GPCR.** A *target-class router* (GPCR ligand / ion-channel toxin / protease target / cytokine or growth-factor receptor / integrin ligand / antimicrobial / unknown) classifies the peptide up front and adapts the enumeration strategy. GPCRs are the best-trodden case (and the validated control), but the pipeline's spine — homology, motif, phenotype, cross-species, co-fold — is target-class-agnostic, and family enumeration uses **HGNC gene-family (general) + InterPro (general) + GPCRdb (GPCR-only cross-check)**.
## Core reasoning: LOOK UP, DON'T GUESS
The failure mode this skill defends against is **guessing a target from the peptide's name or its assumed mechanism**. A peptide can be **phenotypically active yet not bind the hypothesized target** — because it hits a *paralog*, a *different family member*, or the *same receptor in a different species* whose binding interface has diverged. So:
1. **Never assert a target from memory.** Every candidate must come from a tool result (homology hit, family enumeration, phenotype association, or structural co-fold), with the tool name and accession recorded.
2. **Anchor on PHENOTYPE × STRUCTURE/SEQUENCE plausibility, not on the peptide's reputed mechanism.** The real target is the intersection of (a) what the *sequence/motif/structure* says it could bind and (b) what the *phenotype* says is biologically relevant. A name-level guess ("it's a GLP-1 analog so it's GLP1R") is exactly what produces off-target errors.
3. **Reconcile across species.** "Binds in species A but not B" is usually **interface sequence divergence**, not a different target. Always pull the ortholog set and align the candidate receptor's ligand-binding interface across the assay species before concluding the peptide "doesn't work."
4. **A non-binding result against the hypothesized target is a clue, not a dead end.** It promotes the *paralogs* and *phenotype-shared* receptors to the top of the candidate list.View the complete source on GitHubtooluniverse-pharmacogenomics
When your agent uses it
Pharmacogenomics (PGx) research — drug-gene interactions (CPIC, PharmGKB), CPIC dosing guidelines, variant-drug-response associations, ethnic-allele-frequency considerations, and metabolizer-status scoring. Use for PGx-informed dosing recommendations, CYP/HLA pharmacogenomic allele interpretation, and clinically-actionable PGx report generation.
What it loads into context
Frontmatter
86 tokensAlways available to the agent
SKILL.md
5,683 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Workflow Overview
- Phase 0: Disambiguation
- Phase 1: Identify Gene-Drug Pairs
- Phase 2: Retrieve Dosing Guidelines
- Phase 3: Allele & Variant Annotation
- Phase 4: FDA Biomarker Labeling
- Phase 5: Cross-Database Enrichment
- When PGx Testing Changes Clinical Decisions
- Key Parameter Notes
- CPIC Guideline Application Reasoning
- Fallback Strategies
- Example Workflows
- Drug Class Context (RxClass)
- FDA Substance Identification (FDAGSRS)
- Limitations
Files
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Pharmacogenomics (PGx) Research Skill
Systematic PGx analysis: resolve gene-drug pairs, retrieve CPIC dosing guidelines, annotate alleles and variants with PharmGKB, check FDA PGx biomarker labeling, and generate evidence-graded clinical recommendations.
## When to Use
- "What CPIC guidelines exist for CYP2D6?"
- "Get dosing recommendations for codeine based on CYP2D6 poor metabolizer status"
- "Which drugs have FDA pharmacogenomic biomarkers for CYP2C19?"
- "Find PharmGKB clinical annotations for rs1799853"
- "Is this patient's CYP2D6 genotype relevant to tamoxifen dosing?"View the complete source on GitHubtooluniverse-pharmacokinetics
When your agent uses it
Pharmacokinetic (PK) analysis of concentration-time data — non-compartmental analysis (NCA) for Cmax, Tmax, AUC (0-t and 0-∞), terminal half-life, clearance (CL), volume of distribution (Vd), MRT, and absolute bioavailability (F). Also one-compartment fitting. Use when you have plasma/serum drug concentrations over time after a dose and need PK parameters, or to compute bioavailability from IV + oral AUCs. NOT for ADMET property prediction from structure (use tooluniverse-admet-prediction).
What it loads into context
Frontmatter
136 tokensAlways available to the agent
SKILL.md
1,688 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- Step 1 — Prepare the concentration-time data
- Step 2 — Run NCA
- Step 3 — Interpret the parameters
- Step 4 — Absolute bioavailability (F)
- Step 5 — Quality gotchas (state these)
- Honest limitations
- Related skills
Scripts executed separately
scripts/nca_from_csv.py
Files
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# Pharmacokinetic (PK) Analysis — Non-Compartmental Analysis
Turn a **concentration-vs-time** profile after a dose into the standard PK parameters, and compute **bioavailability** from IV + oral data. Non-compartmental analysis (NCA) is the model-independent workhorse used for most PK reporting.
## When to use this
- You have measured plasma/serum (or other matrix) drug concentrations at known times after a dose.
- You need Cmax/Tmax/AUC/half-life/clearance/Vd, or absolute bioavailability F.
- Comparing exposure (AUC, Cmax) between formulations, doses, or routes.
This is **measured-data** PK. For predicting ADMET properties from a chemical structure, use `tooluniverse-admet-prediction`.
## Step 1 — Prepare the concentration-time data
View the complete source on GitHubtooluniverse-pharmacovigilance
When your agent uses it
Drug safety and adverse event analysis — FAERS spontaneous-report mining, FDA black-box warnings, signal detection (PRR, ROR, IC), risk factors by demographic/comorbidity, label change tracking, and SIDER curated label-derived side-effect frequencies. Use for post-market safety surveillance, AE signal investigation, drug-AE association strength scoring, "what side effects does drug X have" questions, and pharmacovigilance reports.
What it loads into context
Frontmatter
102 tokensAlways available to the agent
SKILL.md
4,697 tokensWhen the skill is activated
Reference documents
15,414 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Clinical Reasoning Framework
- Critical Workflow Requirements
- Tool Parameter Reference (CRITICAL)
- Workflow Overview
- Phase 0: Mechanistic Reasoning (DO THIS BEFORE TOOLS)
- Phase 1: Drug Disambiguation
- Phase 2: Adverse Event Profiling (FAERS)
- Phase 3: Label Warning Extraction
- Phase 4: Pharmacogenomic Risk
- Phase 5: Clinical Trial Safety
- Phase 5.5: Pathway & Mechanism Context
- Phase 5.6: Literature Intelligence
- Phase 6: Signal Prioritization
- Output Report
- Evidence Grading
- Fallback Chains
- Completeness Checklist
- References
Files
CHECKLIST.mddoc6,128 bytesEXAMPLES.mddoc15,683 bytesREPORT_TEMPLATES.mddoc8,302 bytesSIGNAL_ANALYSIS.mddoc6,328 bytesSIGNAL_DETECTION.mddoc2,950 bytesSKILL.mdcore19,441 bytesSKILL.mdcore19,441 bytesSKILL.mdcore19,441 bytesTOOLS_REFERENCE.mddoc15,712 bytes
Read SKILL.md
## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Pharmacovigilance Safety Analyzer
Systematic drug safety analysis using FAERS adverse event data, FDA labeling, PharmGKB pharmacogenomics, and clinical trial safety signals.
**KEY PRINCIPLES**:
1. **Report-first approach** - Create report file FIRST, update progressively
2. **Signal quantification** - Use disproportionality measures (PRR, ROR)
3. **Severity stratification** - Prioritize serious/fatal events
4. **Multi-source triangulation** - FAERS, labels, trials, literature
5. **Pharmacogenomic context** - Include genetic risk factors
6. **Actionable output** - Risk-benefit summary with recommendationsView the complete source on GitHubtooluniverse-phewas
When your agent uses it
Cross-ancestry / cross-biobank phenome-wide association (PheWAS) and replication. Given ONE variant (rsID) or ONE gene, look up every phenotype it associates with across European/UK (UKB-TOPMed), Finnish (FinnGen), Japanese (BioBank Japan), and Taiwanese (TPMI) biobanks, plus exome-wide gene-burden PheWAS (Genebass), then judge whether an association replicates across ancestries or is population-specific. Use whenever the user asks "what else is this variant/gene associated with", "does this association replicate in other ancestries / biobanks", "is this effect East-Asian-specific", "pleiotropy of rsXXX", "phenome scan", or wants to compare effect sizes/allele frequencies of a variant across populations. NOT for the forward direction (trait → which SNPs: use the gwas-* skills), NOT for fine-mapping a locus (use tooluniverse-gwas-finemapping), and NOT for single-SNP mechanism tracing in one population (use tooluniverse-gwas-snp-interpretation).
What it loads into context
Frontmatter
245 tokensAlways available to the agent
SKILL.md
2,561 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- The biobank panel
- Workflow
- Interpretation
- Report structure
- Worked example (rs7903146, TCF7L2)
- Limitations
Files
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# Cross-Biobank PheWAS & Replication
A **PheWAS is the inverse of a GWAS**: a GWAS fixes a phenotype and scans variants; a PheWAS fixes a variant (or gene) and scans the entire phenome. The scientific payoff of running it across *several* biobanks is **replication and ancestry resolution** — an association seen in one population that reappears in another (with the same effect direction) is far more credible, while one that appears only in East-Asian cohorts may reflect ancestry-specific LD, allele frequency, or biology.
This skill orchestrates one variant/gene across four ancestries plus a gene-burden layer. **Look up, don't guess** — never assert a variant's phenotype associations or effect sizes from memory; the whole point is the live cross-biobank numbers.
## The biobank panel
| Tool | Biobank | Ancestry | Build | Evidence type |
|---|---|---|---|---|
| `UKBTOPMed_phewas_by_variant` | UKB-TOPMed | European (UK) | GRCh38 | per-phenotype assoc (phecodes, ~1,400) |
| `FinnGen_get_variant_finemapping` | FinnGen | Finnish | GRCh38 | credible-set membership (fine-mapping) |
| `BioBankJapan_phewas_by_variant` | BioBank Japan | Japanese | **GRCh37** | per-phenotype assoc |
| `TPMI_phewas_by_variant` | TPMI | Taiwanese (Han) | GRCh38 | per-phenotype assoc (ICD-based) |View the complete source on GitHubtooluniverse-phylogenetics
When your agent uses it
Phylogenetic analysis — de novo multiple sequence alignment (Clustal Omega/MUSCLE/MAFFT via EBI_msa_align) and neighbour-joining/UPGMA tree building (EBI_build_phylogenetic_tree) from your own sequences, plus tree analysis, treeness, saturation (PhyKIT), parsimony-informative sites, alignment gap analysis, DVMC, long-branch detection, BUSCO orthologs. Uses PhyKIT, Biopython, DendroPy. Use to align a set of sequences, build a tree from sequences or an alignment, or for phylogenetic tree QC, multi-gene phylogenomics, evolutionary-rate analysis, and comparative-genomics studies.
What it loads into context
Frontmatter
161 tokensAlways available to the agent
SKILL.md
9,104 tokensWhen the skill is activated
Reference documents
16,298 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Four traps that produce a confidently wrong number
- RULE ZERO — Check for pre-computed results FIRST
- BUSCO scogs questions (multi-species phylogenomics)
- LOOK UP, DON'T GUESS
- When to Use
- Required Packages
- Workflow Decision Tree
- Quick Reference
- Common Patterns
- Answer Extraction
- Interpretation
- Completeness Checklist
- Analysis conventions
- References
Scripts executed separately
scripts/busco_target_orthologs.pyscripts/format_alignment.pyscripts/phykit_batch.pyscripts/scogs_paired_compare.pyscripts/scogs_phykit_pipeline.pyscripts/tree_statistics.py../../../../skills/tooluniverse-phylogenetics/scripts/phykit_batch.py
Files
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Read SKILL.md
# Phylogenetics and Sequence Analysis
## Four traps that produce a confidently wrong number
Each of these was observed producing a wrong answer *while the correct guidance
was already present further down this file*. Check them before you answer.
1. **PhyKIT prints more than one column, and for `saturation` the two
conventions disagree — state which you used.** `phykit saturation` prints
`saturation <TAB> |saturation-1|`. Its own `--help` is explicit: *"The first
value is the saturation value and the second column is the absolute value of
saturation minus 1."* But several published analyses (and some reference
answers derived from them) report the **second** column as "the saturation
value". The two always sum to 1.0000, which is the tell that you may beView the complete source on GitHubtooluniverse-plant-genomics
When your agent uses it
Plant genomics and biology research — PlantReactome pathways, Ensembl Plants gene structure, POWO species taxonomy, UniProt annotation, KEGG plant pathways. Handles polyploidy (wheat hexaploidy etc.) and homeologous gene copies. Use for crop-gene annotation, plant secondary metabolism queries, and plant-disease/stress-response biology.
What it loads into context
Frontmatter
88 tokensAlways available to the agent
SKILL.md
1,988 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- When to Use
- Core Tools
- Workflow
- Limitations
Files
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# Plant Genomics & Biology
Pipeline for investigating plant genes, metabolic pathways, species taxonomy, and comparative plant biology using ToolUniverse tools.
## Reasoning Strategy
Plant genomes are large (wheat is ~17 Gb, vs. 3 Gb for human) and often polyploid — wheat is hexaploid (AABBDD), meaning there are three homeologous copies of most genes. When comparing plant genes to Arabidopsis, always account for whole-genome duplications: a single Arabidopsis gene may have 2–4 paralogs in a crop species, all potentially with diverged functions. Gene families are massively expanded in plants relative to animals (e.g., receptor-like kinases, cytochrome P450s, transcription factors) — a BLAST hit does not mean functional equivalence. Arabidopsis thaliana is the primary model, but its small genome and rapid life cycle mean some features (wood formation, nitrogen fixation symbiosis, C4 photosynthesis) are absent and must be studied in other species.
**LOOK UP DON'T GUESS**: Do not assume gene function by sequence similarity alone in polyploid species; look up functional validation evidence via UniProt (reviewed entries) or PlantReactome. Do not assume KEGG organism codes — use the table or query `kegg_search_pathway` with the species name to confirm availability.
**Key principles**:
1. **Plant-specific pathways** — photosynthesis, secondary metabolism, hormone signaling are unique to plants
2. **PlantReactome as foundation** — curated plant pathway database with cross-species coverage (Oryza, Arabidopsis, Zea mays, etc.)
3. **Ensembl Plants for genomics** — use Ensembl with plant species names for gene lookup and annotationView the complete source on GitHubtooluniverse-polygenic-risk-score
When your agent uses it
Build and interpret polygenic risk scores (PRS) for complex diseases using GWAS summary statistics. Covers PRS construction (clumping/thresholding, PRS-CS), validation in independent cohorts, ancestry-aware adjustment, and clinical interpretation (population-relative risk, not absolute prediction). Use for PRS-based risk stratification.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
3,946 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Strategy
- Overview
- Methodology
- Data Sources
- Key Concepts
- Applications
- Limitations and Considerations
- Workflow
- Best Practices
- Disclaimer
Scripts executed separately
check_data.pypython_implementation.py
Files
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# Polygenic Risk Score (PRS) Builder
Build and interpret polygenic risk scores for complex diseases using genome-wide association study (GWAS) data.
## Reasoning Strategy
A polygenic risk score predicts genetic risk, not disease. A high PRS means elevated risk relative to the population — it does not mean the person will develop the condition, and a low PRS does not confer immunity. PRS performance varies dramatically across ancestries: a European-derived PRS applied to a West African population can lose 50–70% of its predictive power because the underlying GWAS was trained on European allele frequencies and LD patterns. Effect sizes from discovery GWAS are subject to winner's curse (overestimation in single studies); always prefer weights from large meta-analyses or validated PGS Catalog models. PRS should always be interpreted in the context of non-genetic risk factors — for most complex diseases, environmental factors contribute as much or more than genetics.
**LOOK UP DON'T GUESS**: Do not assume effect sizes, allele frequencies, or which SNPs are genome-wide significant for a trait — always query GWAS Catalog (`gwas_get_associations_for_trait`) for actual data. Do not assume a validated PRS model exists for a trait; check PGS Catalog via PubMed search.
## Overview
**Use Cases:**
- "Calculate my genetic risk for type 2 diabetes"View the complete source on GitHubtooluniverse-population-genetics
When your agent uses it
Population genetics analysis — allele frequencies (gnomAD, 1000 Genomes), Hardy-Weinberg equilibrium testing, Fst between populations, GWAS associations, evolutionary constraint scores. Use for cross-population variant comparison, ancestry-aware allele frequency lookups, and population-level evolutionary analysis.
What it loads into context
Frontmatter
72 tokensAlways available to the agent
SKILL.md
5,577 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use
- LOOK UP, DON'T GUESS
- Tool Quick Reference
- Workflow Patterns
- Theoretical Reasoning (CRITICAL for computation problems)
- Mendelian Genetics Reasoning Framework
- E. coli Hfr Mapping Framework
- MCQ Elimination Strategy for Genetics
- Common Genetics Reasoning Traps
- Advanced Genetics Traps v2
- Bundled Computation Script
- Key Concepts
- Evidence Grading
Scripts executed separately
scripts/popgen_calculator.py
Files
SKILL.mdcore21,602 bytesSKILL.mdcore21,602 bytesscripts/popgen_calculator.pyruns17,228 bytes
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# Population Genetics Analysis
**MC Strategy**: Population genetics MC questions often test whether you know a specific theorem or result. COMPUTE the answer first (use popgen_calculator.py or write Python), then match to options. Don't try to reason about which option "sounds right."
Analyze population-level genetic variation, allele frequencies, GWAS associations, clinical significance, and evolutionary constraints using ToolUniverse tools.
## When to Use
Activate this skill when the user asks about:
- Allele frequencies across populations (gnomAD, 1000 Genomes)
- GWAS associations for diseases/traits
- Clinical variant interpretation (ClinVar, VEP)
- Gene-level constraint metrics (pLI, LOEUF, o/e ratios)
- Selection, drift, linkage disequilibrium, or population structureView the complete source on GitHubtooluniverse-population-genetics-1000genomes
When your agent uses it
Population genetics using the 1000 Genomes Project (IGSR) — superpopulation/population search, sample metadata, variant frequencies across AFR/AMR/EAS/EUR/SAS, ancestry-specific analyses. Use for ancestry comparison, population-aware allele frequency lookups, and 1000-Genomes-cohort-specific analyses (distinct from gnomAD which has different sample composition).
What it loads into context
Frontmatter
187 tokensAlways available to the agent
SKILL.md
2,782 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- NOT for (use other skills instead)
- Phase 1: Search 1000 Genomes Populations
- Phase 2: Search Samples by Population
- Phase 3: List Data Collections
- Phase 4: GWAS Context for Population Stratification
- Workflow: Population Stratification in GWAS
- Common Population Codes
- Reasoning Framework for Result Interpretation
- Tool Parameter Quick Reference
Files
SKILL.mdcore10,768 bytes
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Population Genetics with 1000 Genomes (IGSR)
Use IGSR tools to search 1000 Genomes populations and samples, explore data collections, and
combine with GWAS tools for population-stratified analysis.
## When to Use
- "List all African (AFR) populations in the 1000 Genomes Project"
- "Find samples from the YRI (Yoruba) population"
- "What 1000 Genomes data collections are available?"
- "Which GWAS SNPs for type 2 diabetes have population-specific effects?"View the complete source on GitHubtooluniverse-precision-medicine-stratification
When your agent uses it
Patient stratification for precision medicine — integrate genomic, clinical, and therapeutic data to split patients into responder/non-responder groups, risk tiers, or treatment-decision groups. Use for stratification-by-biomarker, treatment-selection logic, and personalized therapeutic strategy reports per patient subgroup.
What it loads into context
Frontmatter
73 tokensAlways available to the agent
SKILL.md
2,819 tokensWhen the skill is activated
Reference documents
10,057 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Reasoning Before Searching
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parsing
- Critical Tool Parameter Notes
- Workflow Overview
- Phase 1: Disease Disambiguation & Profile Standardization
- Phase 2: Genetic Risk Assessment
- Phase 3: Disease-Specific Molecular Stratification
- Phase 4: Pharmacogenomic Profiling
- Phase 5: Comorbidity & Drug Interaction Risk
- Phase 6: Molecular Pathway Analysis
- Phase 7: Clinical Evidence & Guidelines
- Phase 8: Clinical Trial Matching
- Phase 9: Integrated Scoring & Recommendations
- Common Use Patterns
Files
.env.templateasset499 bytesEXAMPLES.mddoc8,656 bytesQUICK_START.mddoc3,691 bytesREPORT_TEMPLATE.mddoc5,134 bytesSCORING_REFERENCE.mddoc9,471 bytesSKILL.mdcore10,777 bytesSKILL.mdcore10,777 bytesTOOLS_REFERENCE.mddoc8,727 bytes
Read SKILL.md
# Precision Medicine Patient Stratification
Transform patient genomic and clinical profiles into actionable risk stratification, treatment recommendations, and personalized therapeutic strategies.
## Reasoning Before Searching
Stratification means splitting patients into groups that respond differently to a treatment or have different prognoses. Ask these questions before running any tools:
1. **What molecular feature predicts response?** Candidates: somatic mutation (e.g., EGFR L858R), germline variant (e.g., BRCA1 LoF), expression level (e.g., HER2 overexpression), germline pharmacogenomic variant (e.g., CYP2C19 PM), or composite biomarker (e.g., TMB-H + MSI-H).
2. **Is the predictive feature actionable?** Knowing it must change treatment — either the drug choice, dose, or monitoring plan. A variant with prognostic value but no therapeutic consequence is not a stratification biomarker.
3. **What is the evidence level for the stratifier?** FDA-approved companion diagnostic (T1) vs. exploratory (T4) changes how much weight to place on the finding.
Route to the correct Phase 3 path BEFORE running Phase 2 tools — cancer, metabolic, CVD, rare disease, and autoimmune pipelines require different stratifiers.
View the complete source on GitHubtooluniverse-precision-oncology
When your agent uses it
Cancer treatment recommendations from molecular profile (mutations + cancer type + biomarkers) — FDA-approved + investigational therapies, resistance mechanisms, matching clinical trials, prognosis. Uses CIViC, ClinVar, OpenTargets, ClinicalTrials.gov. Use for tumor-board treatment recommendations, evidence-tiered actionability assessment, and FDA-precedent-driven therapy selection.
What it loads into context
Frontmatter
84 tokensAlways available to the agent
SKILL.md
2,960 tokensWhen the skill is activated
Reference documents
16,613 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- Treatment Selection Reasoning
- LOOK UP DON'T GUESS
- When to Use
- Phase 0: Tool Verification
- Workflow Overview
- Key Tools by Phase
- Cross-Skill References
- References
Scripts executed separately
scripts/gdsc_drug_response.py../../../../skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py
Files
API_USAGE_PATTERNS.mddoc18,745 bytesCHECKLIST.mddoc3,104 bytesEXAMPLES.mddoc11,413 bytesREPORT_TEMPLATE.mddoc6,046 bytesSKILL.mdcore11,148 bytesTOOLS_REFERENCE.mddoc19,810 bytesTREATMENT_ALGORITHMS.mddoc2,950 bytesscripts/gdsc_drug_response.pyruns4,653 bytes../../../../skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.pyruns36,294 bytes
Read SKILL.md
# Precision Oncology Treatment Advisor
Provide actionable treatment recommendations for cancer patients based on their molecular profile using CIViC, ClinVar, OpenTargets, ClinicalTrials.gov, and structure-based analysis.
## Domain Reasoning
Treatment selection follows a strict evidence hierarchy: FDA-approved for this specific mutation in this cancer type ranks highest, followed by approval for this mutation in any cancer (tumor-agnostic), then active clinical trials, and finally off-label use. Skipping this hierarchy to recommend off-label therapies when an approved option exists is a clinical error. Always check current NCCN guidelines and recent literature, as approvals change rapidly — a drug that was investigational last year may now be first-line.
When looking up treatment for a specific mutation, search CIViC and OncoKB FIRST, not PubMed. These databases have curated evidence levels. PubMed is for when curated databases don't have the answer.
## Treatment Selection Reasoning
**Biomarker-to-drug logic** — When a biomarker is identified, the first-line targeted therapy follows established mappings. Always verify current approval status via OncoKB/CIViC, but use this as a starting framework:
- **NSCLC**: EGFR exon 19 del / L858R → osimertinib (1L); ALK fusion → alectinib/lorlatinib; ROS1 fusion → crizotinib/entrectinib; KRAS G12C → sotorasib/adagrasib; MET exon 14 skip → capmatinib/tepotinib; RET fusion → selpercatinib; BRAF V600E → dabrafenib+trametinib; NTRK fusion → larotrectinib/entrectinib (tumor-agnostic)View the complete source on GitHubtooluniverse-primer-design
When your agent uses it
PCR / qPCR primer and oligo design — design forward/reverse primers for a target region (SantaLucia nearest-neighbor thermodynamics), compute melting temperature (Tm) and annealing temperature (Ta), check GC content, and screen an oligo for hairpins and primer-dimers. Use when you need primers for a sequence, want to QC an existing primer pair, or need the Tm of an oligo. Covers the primer-design rules (Tm matching, GC clamp, 3'-end, length) and the tools' constraint quirks.
What it loads into context
Frontmatter
126 tokensAlways available to the agent
SKILL.md
1,476 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this
- Step 1 — Design a primer pair
- Step 2 — Get Tm / annealing temperature for specific primers
- Step 3 — Primer design rules (what "good" looks like)
- Step 4 — Specificity (the tools do NOT do this)
- Step 5 — Common gotchas
- Honest limitations
- Related skills
Scripts executed separately
scripts/primer_qc.py
Files
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# PCR / qPCR Primer & Oligo Design
Design primers for a target DNA region, get their Tm/Ta, and QC them for the secondary-structure problems that make a PCR fail.
## When to use this
- Design a forward/reverse primer pair to amplify a region of a sequence.
- Compute the Tm / annealing temperature of a primer.
- QC an existing primer pair (GC clamp, 3'-end, hairpins, self/cross dimers, Tm match).
## Step 1 — Design a primer pair
```bash
tu run DNA_primer_design '{"operation":"primer_design",View the complete source on GitHubtooluniverse-product-safety-surveillance
When your agent uses it
Post-market safety surveillance and recall/adverse-event RETRIEVAL across the full spectrum of FDA-regulated products that are NOT covered by the drug-AE signal skills: medical devices, food / dietary supplements / cosmetics, veterinary drugs, and drug supply (shortages). Orchestrates openFDA endpoints (MAUDE device adverse events + device recalls + 510(k), CAERS food/supplement/ cosmetic adverse events, veterinary adverse events, drug shortages, and cross-product enforcement/recall reports). USE WHEN the user asks: "are there adverse events for [device / pacemaker / infusion pump / insulin pump]", "device recalls for [firm/product]", "supplement / vitamin / cosmetic adverse reactions", "is [drug] in shortage", "what injectables are on shortage", "veterinary / animal adverse events for [drug] in [dog/cat/horse]", "food recall for listeria", "MAUDE report for [device]", "CAERS reactions for [brand]". DO NOT USE for drug adverse-event SIGNAL detection or disproportionality (PRR / ROR /...
What it loads into context
Frontmatter
248 tokensAlways available to the agent
SKILL.md
4,169 tokensWhen the skill is activated
Reference documents
1,550 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use vs When NOT to Use
- Tool Map (which tool for which question)
- openFDA Query Grammar (CRITICAL — read before querying)
- Interpretation Tables (raw openFDA field → meaning)
- Workflow
- Worked Examples (verified live)
- Limitations (state these in every report)
- References
Files
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# Product Safety Surveillance (multi-product, openFDA)
Retrieve and interpret post-market safety records across **every FDA-regulated
product class except drug-AE signal mining**: medical devices, food / dietary
supplements / cosmetics, veterinary drugs, and drug supply (shortages), plus
cross-product enforcement/recall reports.
**KEY PRINCIPLES**
1. **Decide the product class first.** Device? Food/supplement/cosmetic? Vet drug? Drug shortage? Recall? The class picks the tool.
2. **Build a valid Lucene query.** openFDA uses field-scoped `field:value` terms; combine with a space-separated `AND`. Phrases and special characters need care (see Query Grammar).
3. **Retrieve, then interpret.** These are spontaneous/voluntary reports. Report the records and their fields; never assert causation or rates.
4. **Cite every record** with the tool name, the openFDA endpoint, the query used, and the `total` hit count from `meta.results.total`.
5. **Stay in scope.** If the request is drug-AE signal detection (PRR/ROR/IC), STOP and point to `tooluniverse-pharmacovigilance` / `tooluniverse-adverse-event-detection`.
View the complete source on GitHubtooluniverse-protein-design-campaign
When your agent uses it
Run and review iterative protein binder design campaigns with multiple model families, structural and glycan checks, conditional pH hypotheses, resource-aware computation, and competition submission preparation. Use for ongoing candidate optimization or interpreting conflicting design evidence; not for simple structure retrieval or measured assay analysis.
What it loads into context
Frontmatter
69 tokensAlways available to the agent
SKILL.md
1,293 tokensWhen the skill is activated
Reference documents
4,457 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Establish the current decision
- Choose the next informative computation
- Separate evidence dimensions
- Maintain recoverable evidence
- Prepare a reviewed submission
- Completion criteria
Scripts executed separately
scripts/preflight_submission.py
Files
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Read SKILL.md
# Protein Design Campaign
Turn an ongoing binder-design effort into reproducible candidate decisions and an honestly described experimental or competition submission. Preserve the user's chosen models, infrastructure and goals; this skill does not grant permission to upload unpublished designs, publish a repository or accept terms.
## Establish the current decision
For an existing campaign, read its latest status, manifests, executed results and failure records before launching jobs. Distinguish a completed process from an active one; an old PID or a queued browser file is not proof of ongoing computation or submission.
Record target isoform/species, assay construct and termini, intended epitope, glycosylation assumptions, molecule class, designable positions, and objective direction. Explicitly distinguish **stronger binding at acidic pH** from **binding at neutral pH with acidic release**. They are different objectives, not interchangeable consequences of adding histidine.
For competitions, verify the live challenge, account track, deadlines, novelty definition and molecule formats. Convert the deadline with an explicit UTC offset; AoE means UTC−12. Keep current competition limits in the campaign record, not as permanent defaults in this skill.
## Choose the next informative computation
View the complete source on GitHubtooluniverse-protein-interactions
When your agent uses it
Protein-protein interaction (PPI) network analysis — STRING (predicted + experimental), BioGRID (curated), SASBDB (small-angle scattering), Complex Portal (curated complex membership and stoichiometry, includes CORUM data). Distinguishes physical interactions (binding) from functional associations (co-expression, co-regulation). Use for interactome queries, complex partner identification, curated complex composition lookup, and pathway-level interaction analysis.
What it loads into context
Frontmatter
103 tokensAlways available to the agent
SKILL.md
4,218 tokensWhen the skill is activated
Reference documents
13,976 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning: Interaction Type Clarification
- Databases Used
- 4-Phase Workflow
- Complex Portal: Curated Complex Membership
- Parameters
- Network Edge Fields (STRING)
- Extended Analysis Tools
- Tool-Specific Notes
- Domain Reasoning: Multimeric Assemblies & Binding Valency
- Domain Reasoning: Coiled-Coil Oligomeric State Prediction
- Domain Reasoning: Detergent Effects on Membrane Proteins
- Protein Identification Questions
- Troubleshooting
- References
Scripts executed separately
python_implementation.py
Files
.env.templateasset499 bytesDESCRIPTION_UPDATE_COMPLETE.mddoc1,945 bytesDESCRIPTION_UPDATE_PROGRESS.mddoc5,084 bytesDOMAIN_ANALYSIS.mddoc13,105 bytesKNOWN_ISSUES.mddoc3,505 bytesPHASE2_DISCOVERIES.mddoc7,372 bytesQUICK_START.mddoc4,101 bytesSKILL.mdcore17,244 bytesSKILL.mdcore17,244 bytesSKILL.mdcore17,244 bytesSKILL.mdcore17,244 bytesTOOL_DESCRIPTION_OPTIMIZATION.mddoc22,101 bytespython_implementation.pyruns14,741 bytes
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# Protein Interaction Network Analysis
Comprehensive protein interaction network analysis using ToolUniverse tools. Analyzes protein networks through a 4-phase workflow: identifier mapping, network retrieval, enrichment analysis, and optional structural data.
## Domain Reasoning: Interaction Type Clarification
When asked about protein interactions, ask: physical interaction (do they bind?) or functional interaction (do they affect the same pathway)? STRING combines both — a high combined_score does not mean physical binding. For physical binding evidence, check the experimental score (`escore`) specifically. A high `tscore` (text mining) or `dscore` (database) with a low `escore` suggests co-annotation or co-citation, not direct binding.
LOOK UP DON'T GUESS: protein interaction scores, experimental evidence types, and whether two specific proteins have known co-crystal structures. Use STRING `escore` and BioGRID experimental data — do not infer binding from pathway co-membership alone.
## Databases Used
| Database | Coverage | API Key | Purpose |
|----------|----------|---------|---------|View the complete source on GitHubtooluniverse-protein-lof-mechanism
When your agent uses it
Propose the mechanism by which a missense variant causes loss-of-function (LoF), synthesizing evidence from 5 independent layers: AlphaMissense pathogenicity, AlphaFold structural context, ESMC sequence likelihood, SAE feature disruption, and DynaMut2 stability ΔΔG. Distinguishes 'structural stability LoF' (mis-folding) from 'direct functional disruption' (catalytic / binding / PTM site damage). Use for coding missense variants where you need a mechanistic causal model, not just a pathogenicity score.
What it loads into context
Frontmatter
130 tokensAlways available to the agent
SKILL.md
3,514 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Required inputs
- Prerequisites
- Workflow
- Reporting format
- Honest limitations
- Optional: ThermoMPNN instead of DynaMut2 (advanced users with GPU)
Files
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# Protein LoF Mechanism Synthesis
For a single missense variant, integrate 5 independent computational signals to propose a specific loss-of-function mechanism. Each signal answers a different question:
| Signal | Tool | Answers |
|---|---|---|
| Pathogenicity | `AlphaMissense_get_variant_score` | "Is this variant damaging?" |
| Structural context | `alphafold_get_prediction` | "Is the mutation in a folded vs disordered region?" |
| Sequence likelihood | `ESM_score_sequence` | "Is the substitution evolutionarily plausible?" |
| Feature disruption | `ESM_explain_variant_mechanism` (or `ESM_score_variant_sae_disruption` + `ESM_describe_sae_feature` for raw control) | "Which biological feature breaks?" |
| Stability | `DynaMut2_predict_stability` | "Does the protein still fold correctly?" |
---
View the complete source on GitHubtooluniverse-protein-modification-analysis
When your agent uses it
Post-translational modification (PTM) analysis — phosphorylation, ubiquitination, acetylation, glycosylation, methylation. Uses iPTMnet (sites + enzymes), ProtVar (functional consequences), UniProt (baseline), STRING, ELM (linear motifs), MassIVE/ProteomeXchange (experimental), and the EBI Proteins API (mutagenesis, MS-proteomics/HPP peptide evidence, antigenic regions, genomic coordinates, protein-level variant lookup by accession/dbSNP/HGVS) as a cross-validation source. Use for PTM site annotation, kinase-substrate identification, and PTM-disease associations.
What it loads into context
Frontmatter
148 tokensAlways available to the agent
SKILL.md
4,094 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Domain Reasoning
- KEY PRINCIPLES
- Workflow
- Phase 0: Disambiguation
- Phase 1: PTM Sites
- Phase 2: Proteoforms
- Phase 3: PTM-Dependent Interactions
- Phase 4: Functional Context
- Phase 4b: Linear Motifs (ELM)
- Phase 4c: Experimental Data
- Phase 4d: EBI Proteins Cross-Validation (Structural, Antigenic, MS-Peptide Evidence)
- Evidence Grading
- Tool Parameter Reference
- Fallbacks
- Limitations
Files
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# Protein Post-Translational Modification Analysis
Comprehensive PTM analysis using iPTMnet (primary), ProtVar (functional context), UniProt (baseline), STRING (interactions), ELM (linear motifs), and MassIVE/ProteomeXchange (experimental data).
> **iPTMnet is down (checked 2026-10-08).** Its REST API has answered HTTP 503 to every request since at least 2026-09-20 while the website stays up, so every `iPTMnet_*` call below currently returns that error. Try one call; if it is a 503, use the fallbacks given in each phase -- `UniProt_get_ptm_processing_by_accession` for modified residues (with the modifying enzyme where curated), `OmniPath_get_enzyme_substrate` for enzyme-substrate pairs, `EBIProteins_get_proteomics_ptm` for mass-spectrometry evidence. Nothing replaces iPTMnet's proteoform and PTM-dependent-interaction data; say so in the report rather than filling the gap.
## LOOK UP DON'T GUESS
- PTM sites/enzymes: `iPTMnet_get_ptm_sites`
- Functional consequence: `ProtVar_get_function` + `iPTMnet_get_ptm_ppi`
- Proteoforms: `iPTMnet_get_proteoforms`
- Linear motifs: `ELM_get_instances`
## COMPUTE, DON'T DESCRIBEView the complete source on GitHubtooluniverse-protein-sae-variant-interpretation
When your agent uses it
Interpret a missense variant via ESMC-6B Sparse Autoencoder (SAE) feature activations. For a given protein + variant, computes which interpretable SAE features (catalytic, ligand-binding, PTM, structural motif, domain, etc.) are lost or gained at the mutation site. Use when standard pathogenicity scores (AlphaMissense, ClinVar) say a variant is damaging but you need a MECHANISTIC explanation — e.g. 'why is this variant LoF?' Complements (does not replace) variant-interpretation and variant-to-mechanism skills, which focus on ACMG classification or regulatory mechanism.
What it loads into context
Frontmatter
150 tokensAlways available to the agent
SKILL.md
3,099 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Required inputs
- Prerequisites
- Workflow (5 steps)
- Interpretation table
- Honest limitations
- Cross-validation pattern (recommended)
- Reporting format
Files
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# Protein SAE Variant Interpretation
Interpret a single missense variant by comparing reference vs mutant Sparse Autoencoder (SAE) feature activations from the ESMC-6B protein language model. SAE features are interpretable latent dimensions of the model's hidden state — many activate on biologically meaningful patterns (active sites, ligand-binding pockets, PTM sequons, structural motifs).
---
## When to use this skill
Apply when users:
- Ask "why is variant X (a missense) loss-of-function?" and need a mechanistic answer beyond a pathogenicity score
- Have an AlphaMissense / ClinVar "damaging" variant and want to know **which functional feature breaks** (catalytic? binding? PTM site? structural?)
- Want to compare ref vs mutant protein representation at a specific residue
- Are interpreting why a structurally subtle change (single AA) has a big functional impact
View the complete source on GitHubtooluniverse-protein-structural-annotation-pdb
When your agent uses it
Given a PDB structure, produce a per-residue annotation table: which residues sit at a binding interface (vs a partner chain), which line a ligand pocket, which are buried (core) vs solvent-exposed (surface), and optionally secondary structure. This is the structural track drawn under a DMS heatmap and the structural prior SAE feature drops are read against. Use when you need to anchor a variant-interpretation or DMS analysis to the protein's actual physical context.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
1,970 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Required inputs
- Workflow
- Interpretation table
- Honest limitations
- Cross-references
Files
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# Protein structural annotation from a PDB
For each residue of a target protein chain, classify whether it sits at a
binding interface, in a ligand pocket, is buried vs solvent-exposed, and
(optionally) which secondary-structure element it belongs to. This is the
annotation track that anchors any DMS heatmap or per-residue interpretation
to the protein's actual physical context.
---
## When to use this skill
- Building a per-residue annotation track for a DMS heatmap
- Deciding whether a variant of interest is **in an interface, in a pocket, orView the complete source on GitHubtooluniverse-protein-structure-prediction
When your agent uses it
Protein 3D structure prediction from sequence — ESMFold de novo prediction, AlphaFold database retrieval, experimental structures from RCSB, ProtVar variant impact assessment, ProtParam sequence properties. Use for structure prediction when no experimental structure exists, fold-confidence scoring, and structure-guided variant interpretation.
What it loads into context
Frontmatter
77 tokensAlways available to the agent
SKILL.md
4,384 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Input Parameters
- Workflow Overview
- Phase 0: Input Preparation
- Phase 1: Sequence Properties
- Phase 2: De Novo Structure Prediction (ESMFold)
- Phase 3: AlphaFold Reference Model
- Complex / Ligand-Binding Prediction (Boltz, paid -- optional)
- Membrane Topology & Signal Peptide Prediction (DTU tools, optional)
- Phase 4: Experimental Structure Comparison
- Phase 5: Variant Structural Impact (When Variant Provided)
- Phase 6: Quality Synthesis and Report
- Tool Parameter Reference
- Fallback Strategies
- Databases Integrated
- Limitations
Files
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# Protein Structure Prediction and Analysis
End-to-end workflow for protein structure prediction starting from a sequence or UniProt accession. Combines ESMFold de novo prediction, AlphaFold database retrieval, experimental structure benchmarking from RCSB, ProtVar variant impact assessment, and ProtParam sequence property calculation.
**KEY PRINCIPLES**:
1. **Sequence first** — obtain or verify the protein sequence before prediction
2. **ESMFold for fast de novo** — works directly on sequence (up to ~800 residues); no database lookup needed
3. **AlphaFold for reference** — retrieve precomputed AlphaFold model for comparison; use `qualifier` parameter (UniProt accession)
4. **Quality before interpretation** — always report pLDDT scores; do not interpret low-confidence regions as folded
5. **Experimental validation** — compare predictions to RCSB experimental structures when available
6. **ProtVar for variants** — use when the question involves mutations or SNVs affecting structure
7. **English-first queries** — use English protein names in all tool calls; respond in the user's language
## LOOK UP, DON'T GUESSView the complete source on GitHubtooluniverse-protein-structure-retrieval
When your agent uses it
Protein structure retrieval from RCSB PDB, PDBe, AlphaFold, SWISS-MODEL, 3D-Beacons (cross-provider structure aggregator), and PDB-REDO (re-refined X-ray structures), with disambiguation, quality assessment (resolution, R-factor, pLDDT, coverage), and metadata. Distinguishes high-quality experimental (X-ray under 2 Angstrom) vs predicted vs homology-model vs medium-quality structures. Use for fetching protein structures, structure-quality comparison, batch model-availability checks across a gene list, checking every structure provider at once, and selecting structures for drug design or modeling.
What it loads into context
Frontmatter
149 tokensAlways available to the agent
SKILL.md
3,191 tokensWhen the skill is activated
Reference documents
2,494 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- Workflow
- Phase 0: Clarification (When Needed)
- Phase 1: Protein Disambiguation
- Phase 2: Data Retrieval (Internal)
- Phase 3: Report Structure Profile
- Quality Assessment
- Error Handling
- Tool Reference
Files
CHECKLIST.mddoc1,647 bytesSKILL.mdcore12,526 bytesSKILL.mdcore12,526 bytesSKILL.mdcore12,526 bytesSKILL.mdcore12,526 bytesexamples.mddoc7,535 bytes
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# Protein Structure Data Retrieval
Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.
**IMPORTANT**: Always use English terms in tool calls. Respond in the user's language.
**LOOK UP DON'T GUESS**: Never assume PDB IDs, resolution, or availability. Always query RCSB/PDBe and AlphaFold to confirm.
## Domain Reasoning
Not all structures are equal. X-ray <2 A is high-quality for drug design. Cryo-EM 3-4 A is good for fold but not side chains. AlphaFold is excellent for well-folded domains but unreliable for disordered regions. Always check pLDDT (AlphaFold) or resolution (experimental) before drawing conclusions.
## Workflow
View the complete source on GitHubtooluniverse-protein-therapeutic-design
When your agent uses it
AI-guided de novo protein design — RFdiffusion backbone generation, ProteinMPNN sequence design, structure validation (pLDDT, pTM, MPNN scores). Use for designing therapeutic protein binders, novel scaffolds, enzyme variants, and miniprotein/protein-interface design before experimental validation.
What it loads into context
Frontmatter
81 tokensAlways available to the agent
SKILL.md
1,793 tokensWhen the skill is activated
Reference documents
13,317 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Workflow Overview
- Critical Requirements
- NVIDIA NIM Tools
- Supporting Tools
- Folding and Developability Screening
- Completeness Checklist
- Reference Files
Files
../tooluniverse-protein-design-campaign/SKILL.mddoc7,182 bytesCHECKLIST.mddoc6,775 bytesDESIGN_PROCEDURES.mddoc4,117 bytesEXAMPLES.mddoc10,976 bytesSKILL.mdcore7,395 bytesTOOLS_REFERENCE.mddoc16,043 bytesdesign_templates.mddoc2,906 bytes
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# Therapeutic Protein Designer
AI-guided de novo protein design using RFdiffusion backbone generation, ProteinMPNN sequence optimization, and structure validation for therapeutic protein development.
**KEY PRINCIPLES**:
1. **Structure-first** - Generate backbone geometry before sequence
2. **Target-guided** - Design binders with target structure in mind
3. **Iterative validation** - Predict structure to validate designs
4. **Developability-aware** - Consider aggregation, immunogenicity, expression
5. **Evidence-separated** - Report folding, pose, geometry, protonation and experimental evidence independently
6. **Actionable output** - Provide sequences ready for experimental testing
7. **English-first queries** - Always use English terms in tool calls
Therapeutic protein design starts with the target interaction. What binding surface do you need to cover? A small pocket = nanobody or peptide. A large flat surface = designed protein. Stability, immunogenicity, and manufacturability constrain the design space.View the complete source on GitHubtooluniverse-proteomics-analysis
When your agent uses it
Mass-spec proteomics analysis — protein identification, quantification (LFQ, TMT, iTRAQ), differential expression (tumor vs normal, treatment vs control), PTM identification, and pathway enrichment on protein lists. Use when you have proteomics MS output, asking about protein abundance differences, or doing systems-level proteomic interpretation.
What it loads into context
Frontmatter
82 tokensAlways available to the agent
SKILL.md
2,969 tokensWhen the skill is activated
Reference documents
3,812 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- RULE ZERO — Check for pre-computed results FIRST
- When to Use This Skill
- COMPUTE, DON'T DESCRIBE
- Core Capabilities
- Workflow Overview
- Integration with ToolUniverse
- Normal-Tissue Expression Context (ProteomicsDB)
- Quantified Minimums
- Interpretation Framework
- Limitations
- References
- Reference Files
Files
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# Proteomics Analysis
## RULE ZERO — Check for pre-computed results FIRST
Before following any instruction below, scan the data folder for:
- `*_executed.ipynb` → read with `tu run read_executed_notebook '{"data_folder":"<path>","search":"<keyword>"}'` and cite its cell outputs as the authoritative answer
- Pre-computed result files (CSV/TSV with names like `*results*`, `*deseq*`, `*enrich*`, `*stats*`, `*_simplified.csv`) → read directly and report the requested value
- Canonical analysis scripts (`analysis.R`, `run_*.py`, `find_*.R`, `*.Rmd`) → execute as-is and read the output
Only follow this skill's re-analysis recipe below if **none** of the above exist. Re-running from raw data produces different numbers than the published answer and is much slower (often 5-10× turn count).
---
Comprehensive analysis of mass spectrometry-based proteomics data from protein identification through quantification, differential expression, post-translational modifications, and systems-level interpretation.View the complete source on GitHubtooluniverse-proteomics-data-retrieval
When your agent uses it
Find and retrieve proteomics datasets from MassIVE, ProteomeXchange, PRIDE Archive, and the NCI Proteomics Data Commons (PDC). Search by species, keyword, accession, or protein; retrieve detailed metadata (instruments, publications, species, PTMs studied), real file-download URLs (PRIDE), or quantitative abundance matrices with clinical linkage (PDC/CPTAC). Use for locating public proteomics datasets to reanalyze, comparing instrument/protocol coverage across studies, pre-download dataset evaluation, and finding every dataset that identified a specific protein.
What it loads into context
Frontmatter
131 tokensAlways available to the agent
SKILL.md
5,192 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to Use This Skill
- COMPUTE, DON'T DESCRIBE
- KEY PRINCIPLES
- Domain Reasoning: Dataset Quality Assessment
- Core Repositories Integrated
- PeptideAtlas: Peptide-Level Observation Evidence
- Cancer Proteomics via PDC
- PRIDE Archive: Full-Text Search, File Downloads, and Reverse Protein Lookup
- Workflow Overview
- Phase 0: Input Resolution
- Phase 1: Repository Search
- Phase 2: Dataset Detail Retrieval
- Phase 3: Result Synthesis
- Tool Parameter Reference
- Fallback Strategies
- Common Species Taxonomy IDs
- Interpretation Framework
- Limitations
- Integration with Other Skills
- References
Files
SKILL.mdcore21,291 bytes
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# Proteomics Data Retrieval
Find and retrieve metadata for publicly available proteomics datasets from MassIVE and ProteomeXchange
repositories. Supports searching by species, keyword, or accession, and returns detailed dataset metadata
including instruments, publications, species, and post-translational modifications.
## When to Use This Skill
**Triggers**:
- "Find proteomics datasets for [organism/disease/protein]"
- "Search MassIVE for [keyword]"
- "Get details for PXD000001" or "Look up MSV000079514"
- "What public mass spectrometry datasets exist for [topic]?"
- "Find MS datasets with [PTM type] data"View the complete source on GitHubtooluniverse-publish-tool
When your agent uses it
Help the user publish one tool from their shared computer to ToolUniverse so other scientists' AI scientists can find and run it, choosing whether only they, specific colleagues, or everyone can use it. Prepares and tests the tool for them; the user makes the final Publish click. Use when the user says "publish my tool", "put my model in Discover", "let anyone use this tool", "let only my collaborators use this tool", or "unpublish my tool".
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
1,202 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- 1. Make sure the tool is shared first
- 2. Decide who may use it
- 3. Prepare it, then let the user publish
- 4. Check it worked
- Changing or removing it later
Files
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# Publish one tool to ToolUniverse
Publishing puts **one** tool from a computer the user already shares into ToolUniverse, with a page in Discover if they make it public. People can run that one tool; they cannot see the computer's other tools or anything else on it.
The person you are helping is usually a scientist, not a programmer. You do the preparation; the final choice of who can use it, and the Publish click, are the user's on the website. Never ask for passwords, keys or tokens in the chat.
## 1. Make sure the tool is shared first
Publishing needs the tool to be running on a computer connected to ToolUniverse.
- If you are connected to the hosted ToolUniverse (you have `list_remote_tools`), call it and look for the user's computer ("your computer ...") and the tool.
- Otherwise ask the user whether https://connect.aiscientist.tools/remote-servers shows the computer **online** with the tool listed.
If it is not shared yet, do that first with the skill `tooluniverse-share-computer`, then come back.View the complete source on GitHubtooluniverse-rare-disease-diagnosis
When your agent uses it
Rare disease differential diagnosis from patient phenotype — HPO term matching to candidate diseases (Orphanet, OMIM), gene panel prioritization, ACMG variant interpretation, and structure-based variant analysis. Use for diagnostic odyssey assistance, phenotype-to-disease ranking, and genetic-counseling differential generation.
What it loads into context
Frontmatter
78 tokensAlways available to the agent
SKILL.md
2,203 tokensWhen the skill is activated
Reference documents
22,129 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- LOOK UP, DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- Clinical Reasoning Framework (BEFORE Tools)
- Tool Parameter Corrections
- Workflow
- Evidence Grading
- Fallback Chains
- Reference Files
Scripts executed separately
scripts/clinical_patterns.py
Files
CHECKLIST.mddoc6,308 bytesDIAGNOSTIC_WORKFLOW.mddoc23,675 bytesEXAMPLES.mddoc13,697 bytesREPORT_TEMPLATE.mddoc11,129 bytesSKILL.mdcore8,871 bytesSKILL.mdcore8,871 bytesTOOLS_REFERENCE.mddoc29,133 bytesscripts/clinical_patterns.pyruns36,160 bytes
Read SKILL.md
# Rare Disease Diagnosis Advisor
Systematic diagnosis support for rare diseases using phenotype matching, gene panel prioritization, and variant interpretation across Orphanet, OMIM, HPO, ClinVar, and structure-based analysis.
**KEY PRINCIPLES**:
1. **Report-first** - Create report file FIRST, update progressively
2. **Phenotype-driven** - Convert symptoms to HPO terms before searching
3. **Multi-database triangulation** - Cross-reference Orphanet, OMIM, OpenTargets
4. **Evidence grading** - Grade diagnoses by supporting evidence strength
5. **English-first queries** - Always use English terms in tool calls
## LOOK UP, DON'T GUESS
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.
View the complete source on GitHubtooluniverse-rare-disease-genomics
When your agent uses it
Rare disease genomics — disease identification (Orphanet), causative gene discovery, gene-disease validity (GenCC), variant interpretation (ClinVar), and translational research (ClinicalTrials.gov, drug repurposing for orphans). Use for rare-disease-gene curation, novel-gene-discovery analysis, and rare-disease drug-development support.
What it loads into context
Frontmatter
186 tokensAlways available to the agent
SKILL.md
4,655 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- Rare Disease Investigation Strategy
- Variant Prioritization Reasoning (CRITICAL)
- When to Use
- NOT for (use other skills instead)
- Workflow Overview
- Phase 0: Disambiguation
- Phase 1: Disease Characterization
- Phase 2: Phenotype Mapping (HPO)
- Phase 3: Causative Gene Discovery
- Phase 4: Gene-Disease Validity Assessment
- Phase 5: Pathogenic Variant Lookup
- Phase 6: Epidemiology
- Phase 6b: Metabolite-Disease Context (IEM)
- Phase 7: Clinical Trials
- Phase 8: Literature
- Evidence Grading
- Fallback Strategies
- Example Workflows
- Common Mistakes to Avoid
- Limitations
- Completeness Checklist
Files
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Rare Disease Genomics Research
## Rare Disease Investigation Strategy
The order of investigation matters: **phenotype -> disease -> gene -> variant**, not the reverse. When starting from a gene, reverse it: gene -> diseases -> expected phenotypes -> does the patient match?
Resist the urge to skip to ClinVar immediately. A "Pathogenic" ClinVar entry is only meaningful if the gene is actually causative for the disease in question with the right inheritance mode.
## Variant Prioritization Reasoning (CRITICAL)
**LOOK UP DON'T GUESS** -- when uncertain about any gene, variant, or disease association, search the database. Do not rely on memory.View the complete source on GitHubtooluniverse-regulatory-genomics
When your agent uses it
Transcription factor binding, cis-regulatory elements (cCREs), chromatin accessibility, and regulatory annotation using JASPAR (motifs), UniBind, ENCODE (cCREs, ChIP-seq), RegulomeDB (regulatory variant scoring), UCSC, and the Ensembl Regulatory Build (motif instances, evolutionarily constrained elements, TF binding matrices) — plus sequence-based deep-learning prediction of regulatory activity and non-coding variant effects (AlphaGenome, Enformer, Borzoi, ChromBPNet, Evo 2). Use for regulatory element annotation, TF-binding-site prediction, regulatory-region functional impact assessment, and predicting how a non-coding variant or a raw DNA sequence affects expression/chromatin/accessibility. Use this whenever a user asks what regulates a gene, whether a SNP hits a regulatory element, or to predict a non-coding variant's functional effect from sequence.
What it loads into context
Frontmatter
196 tokensAlways available to the agent
SKILL.md
7,177 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Domain Reasoning
- LOOK UP DON'T GUESS
- COMPUTE, DON'T DESCRIBE
- When to Use
- Key Tools
- Workflow
- Tool Parameter Reference
- Common Patterns
- Fallback Strategies
- Limitations
Files
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# Regulatory Genomics Research Skill
Systematic investigation of gene regulation through transcription factor binding, chromatin state, and regulatory element annotation. Integrates JASPAR (TF motifs), ENCODE (functional genomics experiments), RegulomeDB (regulatory variant scoring), and UCSC cCREs.
## Domain Reasoning
Regulatory element identification requires converging lines of evidence: sequence conservation alone is insufficient (many conserved sequences are not regulatory), chromatin accessibility is necessary but not sufficient (open chromatin can be structural), TF binding peaks require motif validation, and eQTL evidence ties the element to a transcriptional outcome. No single data type is sufficient. A high-confidence regulatory element requires at least two independent evidence types, and ideally all four.
## LOOK UP DON'T GUESS
- TF binding motifs: retrieve from `jaspar_search_matrices` and `jaspar_get_matrix`; do not describe motifs from memory.
- Experimental ChIP-seq data: search `ENCODE_search_experiments`; do not assume a TF has been profiled in a given cell type.
- cCRE annotations for a genomic region: call `UCSC_get_encode_cCREs` with exact coordinates; do not guess element types.
- Regulatory impact of a variant: query `RegulomeDB_query_variant`; never estimate regulatory importance from position alone.View the complete source on GitHubtooluniverse-regulatory-variant-analysis
When your agent uses it
Non-coding/regulatory variant interpretation — GWAS association lookup, eQTL evidence (GTEx), chromatin state (ENCODE), regulatory variant scoring (RegulomeDB, CADD), TF-binding disruption, and sequence-based deep-learning prediction (AlphaGenome/AlphaGenome Atlas) for when annotation databases are silent. Use for non-coding GWAS hit interpretation, eQTL-based gene assignment, and regulatory mechanism reasoning. Distinct from coding-variant tools.
What it loads into context
Frontmatter
110 tokensAlways available to the agent
SKILL.md
5,082 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- COMPUTE, DON'T DESCRIBE
- When to Use
- Non-Coding Variant Impact Reasoning
- Workflow Overview
- Phase 0: Variant/Trait Resolution
- Phase 0.5: Sequence-Based Triage (AlphaGenome Atlas)
- Phase 1: GWAS Association Lookup
- Phase 2: eQTL Analysis
- Phase 3: Regulatory Element Annotation
- Phase 4: OpenTargets GWAS Integration
- Phase 4.5: Sequence-Based Deep-Dive (conditional)
- Phase 5: Functional Impact Synthesis
- Fallback Strategies
- Example Workflows
- Limitations
Files
SKILL.mdcore22,019 bytesSKILL.mdcore22,019 bytesSKILL.mdcore22,019 bytesSKILL.mdcore22,019 bytes
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## COMPUTE, DON'T DESCRIBE
When analysis requires computation (statistics, data processing, scoring, enrichment), write and run Python code via Bash. Don't describe what you would do — execute it and report actual results. Use ToolUniverse tools to retrieve data, then Python (pandas, scipy, statsmodels, matplotlib) to analyze it.
# Regulatory Variant Analysis Skill
Systematic regulatory variant interpretation: discover trait associations from GWAS, map eQTL effects, annotate chromatin context, assess regulatory element overlap, predict direct sequence-level effects with AlphaGenome/AlphaGenome Atlas, and produce evidence-graded functional impact predictions for non-coding variants.
## When to Use
- "What GWAS associations exist for rs12913832?"
- "Find eQTLs for the APOE locus in brain tissue"
- "What regulatory elements overlap this variant region?"
- "Which SNPs are associated with type 2 diabetes from GWAS?"
- "Is this intronic variant in an active enhancer?"View the complete source on GitHubtooluniverse-residue-functional-mechanism-interpretation
When your agent uses it
Given a set of residues in a protein, explain WHY they are functionally critical by combining structural context (binding interface, ligand pocket, core, secondary structure), UniProt features (active sites, binding sites, PTM sites, disulfides), optional SAE feature evidence, and optional DMS data. Accepts residues from any source: DMS hotspots (top-K by max effect), ClinVar recurrent variants, literature-reported hot regions, evolutionarily conserved positions, or user-curated lists. Returns a per-cluster mechanism call: catalytic / ligand-binding / interface / structural-core / PTM / regulatory / unknown.
What it loads into context
Frontmatter
144 tokensAlways available to the agent
SKILL.md
5,881 tokensWhen the skill is activated
Reference documents
0 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- When to use this skill
- Required inputs (choose one entry path)
- Workflow
- Interpretation table — what the mechanism call means downstream
- Honest limitations
- Cross-references
Files
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# Residue functional mechanism interpretation
The core user question: **"Why are these residues functionally critical?"**
Answering needs more than one source: a residue in the ligand pocket means
something different from one in a catalytic triad, an interface, the
hydrophobic core, or a PTM sequon. This skill synthesizes evidence from
multiple TU tools to call a mechanism for each residue (or cluster of
adjacent residues).
The residues can come from **any source** — this skill is agnostic to where
they came from:
| Residue source | Typical call pattern |View the complete source on GitHubtooluniverse-rnaseq-deseq2
When your agent uses it
RNA-seq differential expression analysis with DESeq2, edgeR, and limma-voom — DEG lists, fold changes, dispersion estimation, design formulas including covariates, multi-condition contrasts, and Venn-set operations across groups. Routes across DESeq2 (default), edgeR (QL-F / exact test for small replicate counts), and limma-voom (large n / complex designs). Use when you have a count matrix + metadata, want to find DEGs, or need dispersion/PCA/clustering analysis. Includes RULE ZERO precedence (read executed.ipynb if present).
What it loads into context
Frontmatter
138 tokensAlways available to the agent
SKILL.md
9,341 tokensWhen the skill is activated
Reference documents
23,590 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- PRIMARY SCRIPTS — use these FIRST before writing custom code
- CRITICAL — Read before writing any code
- Workspace isolation (CRITICAL)
- Domain Reasoning
- LOOK UP DON'T GUESS
- Core Principles
- When to Use
- Required Packages
- Analysis Workflow
- Common Patterns
- Error Quick Reference
- Interpretation Framework
- Known Limitations
- Reference Files
- Utility Scripts
- Analysis conventions
- Density / per-chromosome calculation conventions
Scripts executed separately
scripts/convert_rds_to_csv.pyscripts/format_deseq2_output.pyscripts/gene_length_correlation.pyscripts/load_count_matrix.pyscripts/multi_strain_venn.pyscripts/one_way_anova_f.pyscripts/pca_variance.pyscripts/r_deseq2_wrapper.pyscripts/r_edger_limma_wrapper.py
Files
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Read SKILL.md
# RNA-seq Differential Expression Analysis (DESeq2)
## PRIMARY SCRIPTS — use these FIRST before writing custom code
The four scripts below are deterministic, audited wrappers that handle the
ambiguity in DESeq2 / correlation / PCA / ANOVA questions by emitting
EVERY common interpretation in one call. Reading their output and
matching the variant the published notebook used is more reliable than
re-deriving the answer from scratch.
All four scripts honor workspace isolation: they ONLY write to `--workdir`
(or `/tmp/...` by default). They never touch the input data folder. Always
pass `--workdir /tmp/<run-name>` when you need intermediate files.
View the complete source on GitHubtooluniverse-scientific-peer-review
When your agent uses it
Review scientific manuscripts, abstracts, methods, statistical analyses, results, discussions, protocols, and experimental designs using calibrated rubrics and prioritized revision advice. Use for journal-style peer review, study-design scoring, methodology or statistics critique, novelty and literature-context assessment, interpretation checks, scientific writing review, reproducibility review, ethics screening, protocol optimization, or a combined manuscript review. This is the host-agent replacement for the corresponding ToolUniverse AgenticTool reviewers and does not require a ToolUniverse backend LLM API.
What it loads into context
Frontmatter
116 tokensAlways available to the agent
SKILL.md
1,249 tokensWhen the skill is activated
Reference documents
2,819 tokensLoaded on request
Token counts are estimates. Scripts run separately; they are not counted as reference context.
The playbook
- Workflow
- Trust Boundary
- Scope and Evidence Boundary
- Review Method
- Long or Multi-Part Manuscripts
- Output
- Quality Checks
Files
SKILL.mdcore6,627 bytesSKILL.mdcore6,627 bytesagents/openai.yamlasset255 bytesreferences/review-rubrics.mddoc7,972 bytesreferences/study-design-guides.mddoc7,346 bytes
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# Scientific Peer Review
Perform the review in the host agent. Do not call the legacy AgenticTool reviewers unless the user explicitly requests legacy/backend execution.
## Workflow
1. Identify the material supplied and the review dimensions requested.
2. Identify the study design. Read [references/study-design-guides.md](references/study-design-guides.md) when design-specific checks apply.
3. Read [references/review-rubrics.md](references/review-rubrics.md) and apply only the relevant rubrics.
4. Separate text-grounded assessment from claims requiring external verification.
5. Assess each requested criterion, cite concrete evidence from the supplied material, and propose actionable revisions.
6. Synthesize the highest-impact issues instead of returning disconnected rubric scores.
## Trust BoundaryView the complete source on GitHubSkills included
- setup-tooluniverse
- tooluniverse
- tooluniverse-acmg-variant-classification
- tooluniverse-admet-prediction
- tooluniverse-adverse-event-detection
- tooluniverse-adverse-outcome-pathway
- tooluniverse-aging-senescence
- tooluniverse-antibody-engineering
- tooluniverse-antigravity-plugin
- tooluniverse-binder-discovery
- tooluniverse-bioinformatics-resource-discovery
- tooluniverse-biomedical-fact-lookup
- tooluniverse-cancer-classification
- tooluniverse-cancer-genomics-tcga
- tooluniverse-cancer-variant-interpretation
- tooluniverse-cell-line-profiling
- tooluniverse-chemical-compound-retrieval
- tooluniverse-chemical-safety
- tooluniverse-chemical-sourcing
- tooluniverse-claude-code-plugin
- tooluniverse-clinical-data-integration
- tooluniverse-clinical-guidelines
- tooluniverse-clinical-risk-scoring
- tooluniverse-clinical-terminology-lookup
- tooluniverse-clinical-trial-design
- tooluniverse-clinical-trial-matching
- tooluniverse-clinical-trial-protocol
- tooluniverse-comparative-genomics
- tooluniverse-computational-biophysics
- tooluniverse-connect
- tooluniverse-crispr-screen-analysis
- tooluniverse-custom-tool
- tooluniverse-data-integration-analysis
- tooluniverse-data-wrangling
- tooluniverse-database-logins
- tooluniverse-dataset-discovery
- tooluniverse-diagnostic-test-evaluation
- tooluniverse-disease-research
- tooluniverse-dose-response
- tooluniverse-drug-drug-interaction
- tooluniverse-drug-mechanism-research
- tooluniverse-drug-regulatory
- tooluniverse-drug-repurposing
- tooluniverse-drug-research
- tooluniverse-drug-synergy
- tooluniverse-drug-target-validation
- tooluniverse-ecology-biodiversity
- tooluniverse-electron-microscopy
- tooluniverse-enzyme-design
- tooluniverse-enzyme-kinetics
- tooluniverse-epidemiological-analysis
- tooluniverse-epigenomics
- tooluniverse-epigenomics-chromatin
- tooluniverse-expression-data-retrieval
- tooluniverse-fastq-qc
- tooluniverse-functional-genomics-screens
- tooluniverse-gene-disease-association
- tooluniverse-gene-enrichment
- tooluniverse-gene-liability
- tooluniverse-gene-regulatory-networks
- tooluniverse-glycobiology
- tooluniverse-gpcr-structural-pharmacology
- tooluniverse-gwas-drug-discovery
- tooluniverse-gwas-finemapping
- tooluniverse-gwas-snp-interpretation
- tooluniverse-gwas-study-explorer
- tooluniverse-gwas-trait-to-gene
- tooluniverse-hla-immunogenomics
- tooluniverse-image-analysis
- tooluniverse-immune-repertoire-analysis
- tooluniverse-immunology
- tooluniverse-immunotherapy-response-prediction
- tooluniverse-infectious-disease
- tooluniverse-inorganic-physical-chemistry
- tooluniverse-install-skills
- tooluniverse-kegg-disease-drug
- tooluniverse-lab-data-standardization
- tooluniverse-lab-protocols
- tooluniverse-lipidomics
- tooluniverse-literature-deep-research
- tooluniverse-medical-imaging-radiology
- tooluniverse-mendelian-randomization
- tooluniverse-meta-analysis
- tooluniverse-metabolomics
- tooluniverse-metabolomics-analysis
- tooluniverse-metabolomics-pathway
- tooluniverse-metagenomics-analysis
- tooluniverse-microbial-genome-characterization
- tooluniverse-microbiome-research
- tooluniverse-ml-inference-utility
- tooluniverse-model-organism-genetics
- tooluniverse-molecular-cloning
- tooluniverse-multi-omics-integration
- tooluniverse-multiomic-disease-characterization
- tooluniverse-natural-product-dereplication
- tooluniverse-network-pharmacology
- tooluniverse-neuroscience
- tooluniverse-neurosynth-meta-analysis
- tooluniverse-nfcore-pipelines
- tooluniverse-nih-funding-landscape
- tooluniverse-noncoding-rna
- tooluniverse-nutrition-food-composition
- tooluniverse-organic-chemistry
- tooluniverse-pathway-disease-genetics
- tooluniverse-peptide-target-deorphanization
- tooluniverse-pharmacogenomics
- tooluniverse-pharmacokinetics
- tooluniverse-pharmacovigilance
- tooluniverse-phewas
- tooluniverse-phylogenetics
- tooluniverse-plant-genomics
- tooluniverse-polygenic-risk-score
- tooluniverse-population-genetics
- tooluniverse-population-genetics-1000genomes
- tooluniverse-precision-medicine-stratification
- tooluniverse-precision-oncology
- tooluniverse-primer-design
- tooluniverse-product-safety-surveillance
- tooluniverse-protein-design-campaign
- tooluniverse-protein-interactions
- tooluniverse-protein-lof-mechanism
- tooluniverse-protein-modification-analysis
- tooluniverse-protein-sae-variant-interpretation
- tooluniverse-protein-structural-annotation-pdb
- tooluniverse-protein-structure-prediction
- tooluniverse-protein-structure-retrieval
- tooluniverse-protein-therapeutic-design
- tooluniverse-proteomics-analysis
- tooluniverse-proteomics-data-retrieval
- tooluniverse-publish-tool
- tooluniverse-rare-disease-diagnosis
- tooluniverse-rare-disease-genomics
- tooluniverse-regulatory-genomics
- tooluniverse-regulatory-variant-analysis
- tooluniverse-residue-functional-mechanism-interpretation
- tooluniverse-rnaseq-deseq2
- tooluniverse-scientific-peer-review
- tooluniverse-scientific-problem-selection
- tooluniverse-scvi-tools
- tooluniverse-sdk
- tooluniverse-self-review
- tooluniverse-sequence-analysis
- tooluniverse-sequence-retrieval
- tooluniverse-share-computer
- tooluniverse-single-cell
- tooluniverse-small-molecule-discovery
- tooluniverse-spatial-omics-analysis
- tooluniverse-spatial-transcriptomics
- tooluniverse-statistical-modeling
- tooluniverse-stem-cell-organoid
- tooluniverse-structural-proteomics
- tooluniverse-structural-variant-analysis
- tooluniverse-synthetic-biology-parts
- tooluniverse-systems-biology
- tooluniverse-target-research
- tooluniverse-toxicology
- tooluniverse-use-shared-computer
- tooluniverse-vaccine-design
- tooluniverse-variant-analysis
- tooluniverse-variant-functional-annotation
- tooluniverse-variant-interpretation
- tooluniverse-variant-predictor-dms-validation
- tooluniverse-variant-to-mechanism
- setup-tooluniverse
- tooluniverse
- tooluniverse-acmg-variant-classification
- tooluniverse-admet-prediction
- tooluniverse-adverse-event-detection
- tooluniverse-adverse-outcome-pathway
- tooluniverse-aging-senescence
- tooluniverse-antibody-engineering
- tooluniverse-antigravity-plugin
- tooluniverse-binder-discovery
- tooluniverse-bioinformatics-resource-discovery
- tooluniverse-biomedical-fact-lookup
- tooluniverse-cancer-classification
- tooluniverse-cancer-genomics-tcga
- tooluniverse-cancer-variant-interpretation
- tooluniverse-cell-line-profiling
- tooluniverse-chemical-compound-retrieval
- tooluniverse-chemical-safety
- tooluniverse-chemical-sourcing
- tooluniverse-clinical-data-integration
- tooluniverse-clinical-guidelines
- tooluniverse-clinical-risk-scoring
- tooluniverse-clinical-terminology-lookup
- tooluniverse-clinical-trial-design
- tooluniverse-clinical-trial-matching
- tooluniverse-clinical-trial-protocol
- tooluniverse-codex-plugin
- tooluniverse-comparative-genomics
- tooluniverse-computational-biophysics
- tooluniverse-connect
- tooluniverse-crispr-screen-analysis
- tooluniverse-custom-tool
- tooluniverse-data-integration-analysis
- tooluniverse-data-wrangling
- tooluniverse-database-logins
- tooluniverse-dataset-discovery
- tooluniverse-diagnostic-test-evaluation
- tooluniverse-disease-research
- tooluniverse-dose-response
- tooluniverse-drug-drug-interaction
- tooluniverse-drug-mechanism-research
- tooluniverse-drug-regulatory
- tooluniverse-drug-repurposing
- tooluniverse-drug-research
- tooluniverse-drug-synergy
- tooluniverse-drug-target-validation
- tooluniverse-ecology-biodiversity
- tooluniverse-electron-microscopy
- tooluniverse-enzyme-design
- tooluniverse-enzyme-kinetics
- tooluniverse-epidemiological-analysis
- tooluniverse-epigenomics
- tooluniverse-epigenomics-chromatin
- tooluniverse-expression-data-retrieval
- tooluniverse-fastq-qc
- tooluniverse-functional-genomics-screens
- tooluniverse-gene-disease-association
- tooluniverse-gene-enrichment
- tooluniverse-gene-liability
- tooluniverse-gene-regulatory-networks
- tooluniverse-glycobiology
- tooluniverse-gpcr-structural-pharmacology
- tooluniverse-gwas-drug-discovery
- tooluniverse-gwas-finemapping
- tooluniverse-gwas-snp-interpretation
- tooluniverse-gwas-study-explorer
- tooluniverse-gwas-trait-to-gene
- tooluniverse-hla-immunogenomics
- tooluniverse-image-analysis
- tooluniverse-immune-repertoire-analysis
- tooluniverse-immunology
- tooluniverse-immunotherapy-response-prediction
- tooluniverse-infectious-disease
- tooluniverse-inorganic-physical-chemistry
- tooluniverse-install-skills
- tooluniverse-kegg-disease-drug
- tooluniverse-lab-data-standardization
- tooluniverse-lab-protocols
- tooluniverse-lipidomics
- tooluniverse-literature-deep-research
- tooluniverse-medical-imaging-radiology
- tooluniverse-mendelian-randomization
- tooluniverse-meta-analysis
- tooluniverse-metabolomics
- tooluniverse-metabolomics-analysis
- tooluniverse-metabolomics-pathway
- tooluniverse-metagenomics-analysis
- tooluniverse-microbial-genome-characterization
- tooluniverse-microbiome-research
- tooluniverse-ml-inference-utility
- tooluniverse-model-organism-genetics
- tooluniverse-molecular-cloning
- tooluniverse-multi-omics-integration
- tooluniverse-multiomic-disease-characterization
- tooluniverse-natural-product-dereplication
- tooluniverse-network-pharmacology
- tooluniverse-neuroscience
- tooluniverse-neurosynth-meta-analysis
- tooluniverse-nfcore-pipelines
- tooluniverse-nih-funding-landscape
- tooluniverse-noncoding-rna
- tooluniverse-nutrition-food-composition
- tooluniverse-organic-chemistry
- tooluniverse-pathway-disease-genetics
- tooluniverse-peptide-target-deorphanization
- tooluniverse-pharmacogenomics
- tooluniverse-pharmacokinetics
- tooluniverse-pharmacovigilance
- tooluniverse-phewas
- tooluniverse-phylogenetics
- tooluniverse-plant-genomics
- tooluniverse-polygenic-risk-score
- tooluniverse-population-genetics
- tooluniverse-population-genetics-1000genomes
- tooluniverse-precision-medicine-stratification
- tooluniverse-precision-oncology
- tooluniverse-primer-design
- tooluniverse-product-safety-surveillance
- tooluniverse-protein-design-campaign
- tooluniverse-protein-interactions
- tooluniverse-protein-lof-mechanism
- tooluniverse-protein-modification-analysis
- tooluniverse-protein-sae-variant-interpretation
- tooluniverse-protein-structural-annotation-pdb
- tooluniverse-protein-structure-prediction
- tooluniverse-protein-structure-retrieval
- tooluniverse-protein-therapeutic-design
- tooluniverse-proteomics-analysis
- tooluniverse-proteomics-data-retrieval
- tooluniverse-publish-tool
- tooluniverse-rare-disease-diagnosis
- tooluniverse-rare-disease-genomics
- tooluniverse-regulatory-genomics
- tooluniverse-regulatory-variant-analysis
- tooluniverse-residue-functional-mechanism-interpretation
- tooluniverse-rnaseq-deseq2
- tooluniverse-scientific-peer-review
Showing 300 of 556 skills. See GitHub for the complete repository.
Repository facts
- License
- Apache-2.0
- Created
- Mar 3, 2025
- Last pushed
- Nov 5, 2026
- Forks
- 273
- Open issues
- 10
- Project activity
- Active
Rank over the last 12 weeks
Now #587 · best #427 · down 82 in 30 days
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Before you install
Network or broad shellAutomated signals, not a guarantee
Checked Nov 5, 2026
Changed on Nov 3, 2026
New signal: Instruction override signal
Not fully analyzed: some files were too long, unreadable or missing. This never lowers the level.
Show 2,704 notes
plugin/hooks/hooks.json:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/setup-tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzedplugin/skills/tooluniverse/SKILL.md:1Analysis incomplete: Referenced executable target unavailable; not analyzed
- No broad tool permissions
- 128 dangerous shell commandsin 58 files: tooluniverse-nfcore-pipelines/scripts/check_environment.py:48 tooluniverse-nfcore-pipelines/scripts/check_environment.py:48 tooluniverse-nfcore-pipelines/scripts/check_environment.py:48 and 55 more
- 3,652 network referencesin 430 files: scripts/remote_validation/setup_skill_preflight.py:38 tooluniverse-data-wrangling/references/specialized-domains.md:26 tooluniverse-data-wrangling/references/specialized-domains.md:26 and 427 more
- 4 obfuscation signalsin 2 files: setup-evo2-remote-tool/SKILL.md:172 setup-progen2-remote-tool/SKILL.md:172
- 561 executable scriptsin 454 files: scripts/remote_validation/setup_skill_preflight.py:1 skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py:1 skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.py:1 and 451 more
- 1,317 references to secretsin 294 files: scripts/remote_validation/setup_skill_preflight.py:464 setup-tooluniverse/API_KEYS_REFERENCE.md:9 setup-tooluniverse/API_KEYS_REFERENCE.md:9 and 291 more
- No requests to vote in Hot
Show all 5,671 signals by file
.claude-plugin/marketplace.json
plugin/.claude-plugin/plugin.json
- Network accessline 5
"author": { "name": "Shanghua Gao", "url": "https://shgao.site" - Network accessline 6
"name": "Shanghua Gao", "url": "https://shgao.site" }, - Network accessline 7
"url": "https://shgao.site" - Network accessline 8
}, "homepage": "https://aiscientist.tools", "repository": "https://github.com/mims-harvard/ToolUniverse", - Network accessline 9
"homepage": "https://aiscientist.tools", - Network accessline 10
"repository": "https://github.com/mims-harvard/ToolUniverse",
plugin/hooks/hooks.json
- Dangerous shell operationline 8
"command": "if [ ! -f ~/.claude/.tooluniverse-plugin-cleanup-done ] && ls ~/.claude/skills/tooluniverse-* 1>/dev/null 2>&1; then rm -rf ~/.claude/skills/tooluniverse-* ~/.claude/skills/create-tooluniverse-skill ~/.claude/skills/setup-toolun - Network accessline 10
{ "type": "command", "command": "command -v uvx >/dev/null 2>&1 || echo 'ToolUniverse: uv/uvx was not found, so the tooluniverse MCP server cannot start and its tools will be unavailable. Install uv with: curl -LsSf - Network accessline 11
"type": "command", "command": "command -v uvx >/dev/null 2>&1 || echo 'ToolUniverse: uv/uvx was not found, so the tooluniverse MCP server cannot start and its tools will be unavailable. Install uv with: curl -LsSf https://astra - Dangerous shell operationline 12
"command": "command -v uvx >/dev/null 2>&1 || echo 'ToolUniverse: uv/uvx was not found, so the tooluniverse MCP server cannot start and its tools will be unavailable. Install uv with: curl -LsSf https://astral.sh/uv/install.sh | sh then - Network accessline 12
"command": "command -v uvx >/dev/null 2>&1 || echo 'ToolUniverse: uv/uvx was not found, so the tooluniverse MCP server cannot start and its tools will be unavailable. Install uv with: curl -LsSf https://astral.sh/uv/install.sh | sh then
plugin/skills/setup-tooluniverse/API_KEYS_REFERENCE.md
- Access to secretsline 9
[Credential-related source omitted] - Network accessline 14
- **What it does**: Increases PubMed API rate limit from 3 requests/second to 10 requests/second. Without it, heavy literature searches may be throttled. - **How to get it**: 1. Go to https://account.ncbi.nlm.nih.gov/ and sign in (via Goo - Network accessline 15
- **How to get it**: 1. Go to https://account.ncbi.nlm.nih.gov/ and sign in (via Google, ORCiD, eRA Commons, or Login.gov) 2. Go to Settings at https://account.ncbi.nlm.nih.gov/settings/ - Network accessline 16
1. Go to https://account.ncbi.nlm.nih.gov/ and sign in (via Google, ORCiD, eRA Commons, or Login.gov) - Network accessline 17
2. Go to Settings at https://account.ncbi.nlm.nih.gov/settings/ - Access to secretsline 21
[Credential-related source omitted] - Access to secretsline 23
[Credential-related source omitted] - Network accessline 28
- **What it does**: Provides access to GPU-accelerated bioinformatics models hosted by NVIDIA. Covers protein structure prediction, molecular docking, and sequence analysis. - **How to get it**: 1. Go to https://build.nvidia.com - Network accessline 29
- **How to get it**: 1. Go to https://build.nvidia.com 2. Sign in or create a free NVIDIA account - Network accessline 30
1. Go to https://build.nvidia.com - Access to secretsline 35
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Network accessline 43
- **What it does**: Enables querying the BioGRID database for protein-protein interactions, genetic interactions, and chemical associations. - **How to get it**: 1. Go to https://webservice.thebiogrid.org/ - Network accessline 44
- **How to get it**: 1. Go to https://webservice.thebiogrid.org/ 2. Click "Register" to create a free account - Network accessline 45
1. Go to https://webservice.thebiogrid.org/ - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Network accessline 56
- **What it does**: Provides access to one of the largest collections of gene-disease associations, integrating data from expert-curated repositories, GWAS catalogs, and animal models. - **How to get it**: 1. Go to https://disgenet.com/ac - Network accessline 57
- **How to get it**: 1. Go to https://disgenet.com/academic-apply (for academic/non-profit use) 2. Fill out the application with your institutional email - Network accessline 58
1. Go to https://disgenet.com/academic-apply (for academic/non-profit use) - Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 70
[Credential-related source omitted] - Network accessline 75
- **What it does**: Provides access to the authoritative database of human genes and genetic phenotypes, particularly for inherited diseases. - **How to get it**: 1. Go to https://omim.org/api - Network accessline 76
- **How to get it**: 1. Go to https://omim.org/api 2. Click "Request API Key" - Network accessline 77
1. Go to https://omim.org/api - Access to secretsline 81
[Credential-related source omitted] - Network accessline 89
- **What it does**: Provides access to MSK's precision oncology knowledge base with FDA-recognized biomarker-drug associations, levels of evidence for therapeutic implications. - **How to get it**: 1. Go to https://www.oncokb.org/apiAcces - Network accessline 90
- **How to get it**: 1. Go to https://www.oncokb.org/apiAccess 2. Click "Request API Access" - Network accessline 91
1. Go to https://www.oncokb.org/apiAccess - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Network accessline 104
- **What it does**: Maps between medical terminologies, finds concept definitions, and navigates hierarchical relationships between medical concepts. - **How to get it**: 1. Go to https://uts.nlm.nih.gov/uts/ - Network accessline 105
- **How to get it**: 1. Go to https://uts.nlm.nih.gov/uts/ 2. Click "Sign Up" to create a UMLS Terminology Services account - Network accessline 106
1. Go to https://uts.nlm.nih.gov/uts/ - Access to secretsline 110
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Network accessline 118
- **What it does**: Enables searching and retrieving US patent documents, patent claims, and patent family information. - **How to get it**: 1. Create a USPTO account at https://my.uspto.gov/ (if you don't have one) - Network accessline 119
- **How to get it**: 1. Create a USPTO account at https://my.uspto.gov/ (if you don't have one) 2. Go to the API manager at https://account.uspto.gov/api-manager/ - Network accessline 120
1. Create a USPTO account at https://my.uspto.gov/ (if you don't have one) - Network accessline 121
2. Go to the API manager at https://account.uspto.gov/api-manager/ - Access to secretsline 124
[Credential-related source omitted] - Access to secretsline 126
[Credential-related source omitted] - Network accessline 131
- **What it does**: Provides an isolated authenticated quota (new keys currently start at 1 request/second) instead of relying on the dynamically throttled anonymous pool. It also enables authentication-required endpoints. - **How to get it - Network accessline 132
- **How to get it**: 1. Go to https://www.semanticscholar.org/product/api 2. Click "Request API Key" - Network accessline 133
1. Go to https://www.semanticscholar.org/product/api - Access to secretsline 137
[Credential-related source omitted] - Access to secretsline 139
[Credential-related source omitted] - Network accessline 144
- **What it does**: Increases openFDA rate limit from 240 requests/minute to 1000 requests/minute. Useful for drug safety signal analysis. - **How to get it**: 1. Go to https://open.fda.gov/apis/authentication/ - Network accessline 145
- **How to get it**: 1. Go to https://open.fda.gov/apis/authentication/ 2. Enter your email to request a key - Network accessline 146
1. Go to https://open.fda.gov/apis/authentication/ - Access to secretsline 149
[Credential-related source omitted] - Network accessline 156
- **What it does**: Provides access to the world's most comprehensive enzyme database with kinetic parameters (Km, Vmax, kcat), substrate/product data, and enzyme classifications. - **How to get it**: 1. Go to https://brenda-enzymes.org/r - Network accessline 157
- **How to get it**: 1. Go to https://brenda-enzymes.org/register.php 2. Fill in email, password, name, and scientific field - Network accessline 158
1. Go to https://brenda-enzymes.org/register.php - Access to secretsline 166
[Credential-related source omitted] - Network accessline 171
- **What it does**: Provides access to Mouser's comprehensive catalog of electronic components, including ICs, resistors, capacitors, microcontrollers, and more. Includes pricing, availability, datasheets, and specifications. - **How to get - Network accessline 172
- **How to get it**: 1. Go to https://www.mouser.com/api-search/ 2. Click "Sign Up For Search API" - Network accessline 173
1. Go to https://www.mouser.com/api-search/ - Access to secretsline 177
[Credential-related source omitted] - Access to secretsline 180
[Credential-related source omitted] - Network accessline 185
- **What it does**: Provides access to Digi-Key's extensive catalog of electronic components with detailed specifications, pricing, inventory, parametric search, and technical documentation. - **How to get it**: 1. Go to https://developer - Network accessline 186
- **How to get it**: 1. Go to https://developer.digikey.com/ 2. Click "Register" to create a free developer account - Network accessline 187
1. Go to https://developer.digikey.com/ - Network accessline 189
3. After login, go to "Organization" → "Production Apps" 4. Click "Create Production App" 5. Fill in app details (name, description, OAuth callback URL: `https://localhost/callback`) - Network accessline 190
4. Click "Create Production App" 5. Fill in app details (name, description, OAuth callback URL: `https://localhost/callback`) 6. After creation, copy the **Client ID** and **Client Secret** - Network accessline 191
5. Fill in app details (name, description, OAuth callback URL: `https://localhost/callback`) - Access to secretsline 193
[Credential-related source omitted] - Access to secretsline 205
[Credential-related source omitted] - Network accessline 209
- **What it does**: Powers agentic tools using Google's Gemini models. Good default choice due to generous free tier. - **How to get it**: 1. Go to https://aistudio.google.com/apikey - Network accessline 210
- **How to get it**: 1. Go to https://aistudio.google.com/apikey 2. Sign in with your Google account - Network accessline 211
1. Go to https://aistudio.google.com/apikey - Access to secretsline 215
[Credential-related source omitted] - Access to secretsline 218
[Credential-related source omitted] - Network accessline 222
- **What it does**: Provides access to 100+ LLM models through a single API. Pay-per-use pricing, good flexibility. - **How to get it**: 1. Go to https://openrouter.ai/ - Network accessline 223
- **How to get it**: 1. Go to https://openrouter.ai/ 2. Sign up or log in - Network accessline 224
1. Go to https://openrouter.ai/ - Network accessline 225
2. Sign up or log in 3. Go to https://openrouter.ai/keys 4. Click "Create Key" - Network accessline 226
3. Go to https://openrouter.ai/keys - Access to secretsline 229
[Credential-related source omitted] - Access to secretsline 231
[Credential-related source omitted] - Network accessline 235
- **What it does**: Powers embedding-based tool finding and can serve as an LLM provider for agentic features. - **How to get it**: 1. Go to https://platform.openai.com/ - Network accessline 236
- **How to get it**: 1. Go to https://platform.openai.com/ 2. Sign up or log in - Network accessline 237
1. Go to https://platform.openai.com/ - Access to secretsline 242
[Credential-related source omitted] - Access to secretsline 244
[Credential-related source omitted] - Network accessline 248
- **What it does**: Same capabilities as OpenAI but through Azure infrastructure. Preferred for enterprise/institutional users. - **How to get it**: 1. Go to Azure Portal (https://portal.azure.com) - Network accessline 249
- **How to get it**: 1. Go to Azure Portal (https://portal.azure.com) 2. Create or navigate to your Azure OpenAI resource - Network accessline 250
1. Go to Azure Portal (https://portal.azure.com) - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 257
[Credential-related source omitted] - Network accessline 261
- **What it does**: Enables Claude-based features. - **How to get it**: 1. Go to https://console.anthropic.com/ - Network accessline 262
- **How to get it**: 1. Go to https://console.anthropic.com/ 2. Sign up or log in - Network accessline 263
1. Go to https://console.anthropic.com/ - Access to secretsline 267
[Credential-related source omitted] - Network accessline 273
- **What it does**: Enables access to gated HuggingFace models and datasets, and the HF Inference API for embeddings. - **How to get it**: 1. Go to https://huggingface.co/settings/tokens - Network accessline 274
- **How to get it**: 1. Go to https://huggingface.co/settings/tokens 2. Sign up or log in - Network accessline 275
1. Go to https://huggingface.co/settings/tokens - Access to secretsline 280
[Credential-related source omitted] - Access to secretsline 289
[Credential-related source omitted] - Access to secretsline 290
[Credential-related source omitted] - Access to secretsline 291
[Credential-related source omitted] - Access to secretsline 292
[Credential-related source omitted] - Access to secretsline 293
[Credential-related source omitted] - Access to secretsline 295
[Credential-related source omitted] - Access to secretsline 296
[Credential-related source omitted] - Access to secretsline 297
[Credential-related source omitted] - Access to secretsline 298
[Credential-related source omitted]
plugin/skills/setup-tooluniverse/CLAUDE_DESKTOP.md
- Network accessline 5
Claude Desktop is a GUI app. It launches in a clean environment and **does not inherit your shell's PATH**. This means if `uvx` lives in `~/.local/bin/uvx` (the default for the uv curl installer), Claude Desktop cannot find it — and will sh - Network accessline 19
If you already installed uv via the curl installer, also run: - Network accessline 55
- Homebrew Apple Silicon: `/opt/homebrew/bin/uvx` - Homebrew Intel: `/usr/local/bin/uvx` - curl installer: `/Users/yourname/.local/bin/uvx` - Network accessline 57
- curl installer: `/Users/yourname/.local/bin/uvx` - Dangerous shell operationline 81
sudo ln -sf "$(which uvx)" /usr/local/bin/uvx - Network accessline 109
If the user installed uv via curl (not Homebrew), replace `'command': 'uvx'` with `'command': '/Users/yourname/.local/bin/uvx'` using their actual path from `which uvx`. - Network accessline 149
If `npx` is not found, install Node.js from [nodejs.org](https://nodejs.org) (LTS version includes npx), then retry. - Network accessline 153
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 157
rm -rf /tmp/tu-skills - Network accessline 178
✅ **Pass**: You see folders like `tooluniverse`, `tooluniverse-drug-research`, etc. → skills are ready. ❌ **Fail**: Nothing listed → wrong directory or install didn't complete. Check [SKILLS_CATALOG.md](https://raw.githubusercontent.com/mim - Network accessline 179
❌ **Fail**: Nothing listed → wrong directory or install didn't complete. Check [SKILLS_CATALOG.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/SKILLS_CATALOG.md) and retry.
plugin/skills/setup-tooluniverse/EXAMPLES.md
- Network accessline 111
git clone https://github.com/mims-harvard/ToolUniverse.git - Dangerous shell operationline 232
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 232
curl -LsSf https://astral.sh/uv/install.sh | sh - Dangerous shell operationline 290
sudo apt install python3.12 python3.12-pip - Access to secretsline 373
[Credential-related source omitted] - Access to secretsline 374
[Credential-related source omitted] - Access to secretsline 375
[Credential-related source omitted] - Access to secretsline 388
[Credential-related source omitted] - Access to secretsline 389
[Credential-related source omitted]
plugin/skills/setup-tooluniverse/GITHUB_ISSUE.md
- Network accessline 44
> > **Step 1.** Open this link in your browser: > https://github.com/mims-harvard/ToolUniverse/issues/new - Network accessline 45
> **Step 1.** Open this link in your browser: > https://github.com/mims-harvard/ToolUniverse/issues/new > - Network accessline 46
> https://github.com/mims-harvard/ToolUniverse/issues/new
plugin/skills/setup-tooluniverse/INSTALL.md
- Network accessline 23
# Install uv first curl -LsSf https://astral.sh/uv/install.sh | sh - Dangerous shell operationline 24
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 24
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 42
git clone https://github.com/mims-harvard/ToolUniverse.git - Dangerous shell operationline 97
sudo apt update - Dangerous shell operationline 98
sudo apt install python3.12 python3.12-pip - Network accessline 104
Download from [python.org](https://www.python.org/downloads/) and select version 3.12. - Dangerous shell operationline 182
rm -rf ~/.cache/tooluniverse
plugin/skills/setup-tooluniverse/MCP_CONFIG.md
- Network accessline 65
For full Trae setup instructions (including manual write fallback and verification), see [TRAE.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TRAE.md). - Access to secretsline 253
[Credential-related source omitted] - Access to secretsline 254
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[Credential-related source omitted] - Network accessline 378
- Verify uvx works: `uvx --version` - Test ToolUniverse directly: `uvx tooluniverse --help` - If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - Network accessline 379
- Test ToolUniverse directly: `uvx tooluniverse --help` - If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - For pip-based installs: `pip show tooluniverse` and `which tooluniverse-smcp-stdio` - Dangerous shell operationline 380
- If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - Network accessline 380
- If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - Access to secretsline 436
[Credential-related source omitted]
plugin/skills/setup-tooluniverse/SKILL.md
- Network accessline 42
**If Chat mode**, ask which app (AskQuestion): Cursor, Claude Desktop, VS Code/Copilot, Windsurf, Claude Code, Gemini CLI, Codex, Cline/Trae/Antigravity/OpenCode. "I don't have any" → Recommend [Claude Desktop](https://claude.ai/download). - Dangerous shell operationline 51
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 51
curl -LsSf https://astral.sh/uv/install.sh | sh - Access to secretsline 139
[Credential-related source omitted] - Network accessline 153
Read https://aiscientist.tools/setup.md and set up ToolUniverse for me. - Network accessline 240
| Key | Unlocks | Free? | Registration | |-----|---------|-------|-------------| | `NCBI_API_KEY` | PubMed (rate limit 3→10/s) | Yes | https://account.ncbi.nlm.nih.gov/settings/ | - Network accessline 241
|-----|---------|-------|-------------| | `NCBI_API_KEY` | PubMed (rate limit 3→10/s) | Yes | https://account.ncbi.nlm.nih.gov/settings/ | | `NVIDIA_API_KEY` | 16 tools: AlphaFold2, docking, genomics | Yes | https://build.nvidia.com | - Network accessline 242
[Credential-related source omitted] - Access to secretsline 242
[Credential-related source omitted] - Network accessline 243
[Credential-related source omitted] - Access to secretsline 243
[Credential-related source omitted] - Network accessline 244
[Credential-related source omitted] - Access to secretsline 244
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[Credential-related source omitted] - Access to secretsline 245
[Credential-related source omitted] - Network accessline 249
| Key | Unlocks | Registration | |-----|---------|-------------| | `DISGENET_API_KEY` | Gene-disease associations | https://disgenet.com/academic-apply | - Network accessline 250
|-----|---------|-------------| | `DISGENET_API_KEY` | Gene-disease associations | https://disgenet.com/academic-apply | | `OMIM_API_KEY` | Mendelian/rare disease | https://omim.org/api | - Network accessline 251
[Credential-related source omitted] - Access to secretsline 251
[Credential-related source omitted] - Network accessline 252
[Credential-related source omitted] - Access to secretsline 252
[Credential-related source omitted] - Network accessline 253
| `ONCOKB_API_TOKEN` | Precision oncology | https://www.oncokb.org/apiAccess | - Network accessline 254
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 264
[Credential-related source omitted] - Access to secretsline 270
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[Credential-related source omitted] - Network accessline 300
git clone --depth 1 https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 313
Clean up: `rm -rf /tmp/tu-skills` - Access to secretsline 393
[Credential-related source omitted] - Access to secretsline 394
[Credential-related source omitted] - Network accessline 431
Still stuck? [GitHub issues](https://github.com/mims-harvard/ToolUniverse/issues) or email [Shanghua Gao](mailto:shanghuagao@gmail.com). - Network accessline 436
- **Upgrade**: `uv cache clean tooluniverse` + restart - **All scientific API keys are free** - **Skills**: https://github.com/mims-harvard/ToolUniverse/tree/main/skills - Network accessline 438
- **Skills**: https://github.com/mims-harvard/ToolUniverse/tree/main/skills
plugin/skills/setup-tooluniverse/TRAE.md
- Dangerous shell operationline 20
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 20
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 26
powershell -c "irm https://astral.sh/uv/install.ps1 | iex" - Network accessline 35
This should print help text. If it fails, see [TROUBLESHOOTING.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TROUBLESHOOTING.md). - Network accessline 129
If `npx` is not found, install Node.js from [nodejs.org](https://nodejs.org) (the LTS version includes npm and npx), then retry. - Network accessline 133
If `git` is not found, install it from [git-scm.com](https://git-scm.com), then retry. - Network accessline 137
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 140
rm -rf /tmp/tu-skills - Network accessline 145
# Run each line separately — PowerShell does not support && like bash git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git "$env:TEMP\tu-skills" - Network accessline 146
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git "$env:TEMP\tu-skills" - Network accessline 154
> Note: The exact skills directory for Trae may vary. Check [SKILLS_CATALOG.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/SKILLS_CATALOG.md) if skills don't activate after installation. - Network accessline 198
2. `uvx tooluniverse --help` works in your **system terminal** (not Trae's terminal) 3. Trae was fully restarted 4. Fetch [TROUBLESHOOTING.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TROUBL - Network accessline 200
4. Fetch [TROUBLESHOOTING.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TROUBLESHOOTING.md) for more diagnostics, or [GITHUB_ISSUE.md](https://raw.githubusercontent.com/mims-harvard/ToolUnive
plugin/skills/setup-tooluniverse/TROUBLESHOOTING.md
- Dangerous shell operationline 12
# or: sudo apt install python3.12 # Ubuntu/Debian - Dangerous shell operationline 22
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 22
curl -LsSf https://astral.sh/uv/install.sh | sh - Dangerous shell operationline 64
sudo ln -sf "$(which uvx)" /usr/local/bin/uvx # Intel Mac / Linux - Dangerous shell operationline 66
sudo ln -sf "$(which uvx)" /opt/homebrew/bin/uvx - Access to secretsline 100
[Credential-related source omitted] - Access to secretsline 106
[Credential-related source omitted] - Access to secretsline 108
[Credential-related source omitted] - Network accessline 183
# In your MCP config args: "args": ["tooluniverse==<last-known-good-version>"] # Check releases: https://github.com/mims-harvard/ToolUniverse/releases - Network accessline 185
# Check releases: https://github.com/mims-harvard/ToolUniverse/releases - Network accessline 224
body = "\n".join(lines) title = "Bug: <brief description>" url = ("https://github.com/mims-harvard/ToolUniverse/issues/new" - Network accessline 225
title = "Bug: <brief description>" url = ("https://github.com/mims-harvard/ToolUniverse/issues/new" "?title=" + urllib.parse.quote(title) - Network accessline 226
url = ("https://github.com/mims-harvard/ToolUniverse/issues/new" "?title=" + urllib.parse.quote(title) + "&body=" + urllib.parse.quote(body)) - Network accessline 242
The script prints two things: 1. **Issue body** — copy-paste it into https://github.com/mims-harvard/ToolUniverse/issues/new - Network accessline 243
1. **Issue body** — copy-paste it into https://github.com/mims-harvard/ToolUniverse/issues/new
plugin/skills/setup-tooluniverse/scripts/check_prerequisites.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 78
else: print(" ⚠<U+FE0F> NOT FOUND: uv not installed (recommended for MCP)") print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh") - Network accessline 79
print(" ⚠<U+FE0F> NOT FOUND: uv not installed (recommended for MCP)") print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh") return False - Dangerous shell operationline 80
print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh") - Network accessline 80
print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh")
plugin/skills/setup-tooluniverse/scripts/diagnose_setup.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/setup-tooluniverse/scripts/list_tool_categories.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/setup-tooluniverse/scripts/verify_installation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-acmg-variant-classification/SKILL.md
- Access to secretsline 112
[Credential-related source omitted]
plugin/skills/tooluniverse-antibody-engineering/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
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[Credential-related source omitted]
plugin/skills/tooluniverse-antibody-engineering/scripts/developability.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-antigravity-plugin/SKILL.md
- Dangerous shell operationline 14
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 14
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 15
agy --version # Antigravity CLI; if missing: https://antigravity.google - Network accessline 32
git clone https://github.com/mims-harvard/ToolUniverse.git - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 88
[Credential-related source omitted] - Access to secretsline 89
[Credential-related source omitted] - Network accessline 109
Still stuck: https://github.com/mims-harvard/ToolUniverse/issues
plugin/skills/tooluniverse-binder-discovery/CHECKLIST.md
- Access to secretsline 23
[Credential-related source omitted]
plugin/skills/tooluniverse-binder-discovery/EXAMPLES.md
- Access to secretsline 385
[Credential-related source omitted] - Network accessline 456
# (no dedicated PDB-download tool; fetch the file from RCSB) pdb_content = tu.tools.download_text_content( url="https://files.rcsb.org/download/2W9Z.pdb" - Network accessline 457
pdb_content = tu.tools.download_text_content( url="https://files.rcsb.org/download/2W9Z.pdb" )['content'] - Network accessline 458
url="https://files.rcsb.org/download/2W9Z.pdb" - Access to secretsline 711
[Credential-related source omitted] - Access to secretsline 715
[Credential-related source omitted]
plugin/skills/tooluniverse-binder-discovery/SKILL.md
- Access to secretsline 92
[Credential-related source omitted] - Access to secretsline 93
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[Credential-related source omitted] - Access to secretsline 142
[Credential-related source omitted] - Access to secretsline 167
[Credential-related source omitted] - Network accessline 258
# PubChem batch property retrieval (up to 100 CIDs per call) cids = "2244,5988,3672" url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" - Network accessline 259
cids = "2244,5988,3672" url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" props = pd.DataFrame(requests.get(url).json()["PropertyTable"]["Prope - Network accessline 260
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" - Network accessline 261
props = pd.DataFrame(requests.get(url).json()["PropertyTable"]["Properties"]) - Network accessline 263
# ChEMBL bioactivity bulk download for a target target_id = "CHEMBL203" # EGFR url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" - Network accessline 264
target_id = "CHEMBL203" # EGFR url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" activities = requests.get(url).json()["activities"] - Network accessline 265
url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" activities = requests.get(url).json()["activities"] df = pd.DataFrame(activities)[["molecule_chembl_id", "canonical_sm - Network accessline 266
activities = requests.get(url).json()["activities"] - Network accessline 273
# SDF download from PubChem (for docking input) sdf_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/SDF" - Network accessline 274
sdf_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/SDF" - Network accessline 275
sdf_content = requests.get(sdf_url).text - Access to secretsline 286
[Credential-related source omitted]
plugin/skills/tooluniverse-binder-discovery/TOOLS_REFERENCE.md
- Access to secretsline 214
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 472
[Credential-related source omitted] - Access to secretsline 580
[Credential-related source omitted] - Access to secretsline 703
[Credential-related source omitted] - Access to secretsline 972
[Credential-related source omitted] - Access to secretsline 973
[Credential-related source omitted]
plugin/skills/tooluniverse-binder-discovery/WORKFLOW_DETAILS.md
plugin/skills/tooluniverse-bioinformatics-resource-discovery/SKILL.md
- Network accessline 48
Verified live: `SmartAPI_search_apis {"query": "biothings", "limit": 3}` -> real APIs including "Biothings Therapeutic Target Database API" (`base_url: https://biothings.transltr.io/ttd`) and "MyGeneset.info API". Feeding that `api_id` into
plugin/skills/tooluniverse-biomedical-fact-lookup/SKILL.md
plugin/skills/tooluniverse-cancer-genomics-tcga/SKILL.md
- Network accessline 302
offset = 0 while True: resp = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 303
while True: resp = requests.post("https://api.gdc.cancer.gov/cases", json={ "filters": filters, "size": 500, "from": offset, - Network accessline 304
resp = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 314
# Download MAF mutation file by UUID file_uuid = "abc123-..." # from GDC_list_files result url = f"https://api.gdc.cancer.gov/data/{file_uuid}" - Network accessline 315
file_uuid = "abc123-..." # from GDC_list_files result url = f"https://api.gdc.cancer.gov/data/{file_uuid}" content = requests.get(url, headers={"Content-Type": "application/json"}).content - Network accessline 316
url = f"https://api.gdc.cancer.gov/data/{file_uuid}" - Network accessline 317
content = requests.get(url, headers={"Content-Type": "application/json"}).content
plugin/skills/tooluniverse-cancer-variant-interpretation/SKILL.md
- Access to secretsline 124
[Credential-related source omitted]
plugin/skills/tooluniverse-cell-line-profiling/SKILL.md
- Network accessline 137
**Per-cell-line Chronos scores** (what the API can't give you): use the bundled script `scripts/depmap_gene_dependency.py`. It pulls the current DepMap Public release (CRISPRGeneEffect.csv + Model.csv) once via the public download index, ca
plugin/skills/tooluniverse-cell-line-profiling/scripts/depmap_gene_dependency.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 37
DEPMAP_INDEX = "https://depmap.org/portal/api/download/files" - Access to secretsline 38
[Credential-related source omitted] - Network accessline 49
with urllib.request.urlopen(DEPMAP_INDEX, timeout=60) as fh: - Network accessline 55
f"DepMap's download index ({DEPMAP_INDEX}) returned a web page instead of " f"the file list, so {filename} cannot be fetched automatically. Download " f"CRISPRGeneEffect.csv and Model.csv from https://depmap.org/port - Network accessline 56
f"the file list, so {filename} cannot be fetched automatically. Download " f"CRISPRGeneEffect.csv and Model.csv from https://depmap.org/portal/download/all/ " f"in a browser, put them in {CACHE_DIR} (or set DEPMAP_CA - Network accessline 57
f"CRISPRGeneEffect.csv and Model.csv from https://depmap.org/portal/download/all/ " - Network accessline 77
if not os.path.exists(path) or os.path.getsize(path) == 0: sys.stderr.write(f"Downloading {filename} (one-time; CRISPRGeneEffect is large)...\n") urllib.request.urlretrieve(_latest_url(filename), path) - Network accessline 78
sys.stderr.write(f"Downloading {filename} (one-time; CRISPRGeneEffect is large)...\n") urllib.request.urlretrieve(_latest_url(filename), path) return path - Network accessline 79
urllib.request.urlretrieve(_latest_url(filename), path)
plugin/skills/tooluniverse-claude-code-plugin/SKILL.md
- Dangerous shell operationline 14
uv --version # must exist; if not: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 14
uv --version # must exist; if not: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 15
claude --version # Claude Code CLI; if not: https://claude.com/claude-code - Dangerous shell operationline 62
rm -rf ~/.claude/skills/tooluniverse-* - Dangerous shell operationline 63
rm -rf ~/.claude/skills/create-tooluniverse-skill - Dangerous shell operationline 64
rm -rf ~/.claude/skills/setup-tooluniverse - Network accessline 75
Replace `1.2.2` with any released version from https://pypi.org/project/tooluniverse/. Restart Claude Code to apply. (`.mcp.json` location is shown under "API keys" below.) - Access to secretsline 131
[Credential-related source omitted] - Access to secretsline 132
[Credential-related source omitted] - Network accessline 168
git clone https://github.com/mims-harvard/ToolUniverse.git - Network accessline 176
Grab `tooluniverse-plugin-vX.Y.Z.zip` from https://github.com/mims-harvard/ToolUniverse/releases, unzip, then: - Network accessline 195
Still stuck: https://github.com/mims-harvard/ToolUniverse/issues - Network accessline 205
# 1. Is uv/uvx installed? (most common cause of "no tools at all") command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Dangerous shell operationline 206
command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Network accessline 206
command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Dangerous shell operationline 223
ls ~/.claude/skills/tooluniverse-* 2>/dev/null && echo "FIX: rm -rf ~/.claude/skills/tooluniverse-*"
plugin/skills/tooluniverse-clinical-guidelines/SKILL.md
- Access to secretsline 124
[Credential-related source omitted]
plugin/skills/tooluniverse-clinical-guidelines/TOOLS_REFERENCE.md
- Access to secretsline 52
[Credential-related source omitted] - Network accessline 196
- **Returns**: dict with patient guideline text - **⚠<U+FE0F> CRITICAL**: Pass full URL, NOT integer ID - **Example**: `NCCN_get_patient_guideline(url='https://www.nccn.org/patientresources/patient-resources/guidelines-for-patients/guidelines-for- - Network accessline 198
- **Example**: `NCCN_get_patient_guideline(url='https://www.nccn.org/patientresources/patient-resources/guidelines-for-patients/guidelines-for-patients-details?patientGuidelineId=61')`
plugin/skills/tooluniverse-clinical-terminology-lookup/SKILL.md
- Access to secretsline 124
[Credential-related source omitted] - Network accessline 422
"22298006"}` -> `"Myocardial infarction"`. The standout use is subsumption expansion: `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006"}` returns every clinical - Network accessline 423
subsumption expansion: `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006"}` returns every clinical subtype (verified live: "Old myocardial infarction", "Acute myocardial - Network accessline 424
"http://snomed.info/sct?fhir_vs=isa/22298006"}` returns every clinical
plugin/skills/tooluniverse-clinical-terminology-lookup/references/clinical_tables_tool_reference.md
- Network accessline 202
"error", "error": "ICD API authentication required. Set ICD_CLIENT_ID and ICD_CLIENT_SECRET environment variables. Register at: https://icd.who.int/icdapi"}`. Same for `ICD11_get_entity` (2/2 failed) - Network accessline 203
[Credential-related source omitted] - Access to secretsline 203
[Credential-related source omitted] - Network accessline 204
https://icd.who.int/icdapi"}`. Same for `ICD11_get_entity` (2/2 failed) - Network accessline 214
for a user is the same either way (a clear error, no fabricated data), just via a different code path than the more common gating pattern. Register free at https://icd.who.int/icdapi to use these. - Network accessline 216
Register free at https://icd.who.int/icdapi to use these. - Network accessline 361
All 4 tools type `BioPortalTool`, no API key required. `get_concept`/ `get_hierarchy` need a full IRI (`http://purl.obolibrary.org/obo/DOID_1909` - Network accessline 362
`get_hierarchy` need a full IRI (`http://purl.obolibrary.org/obo/DOID_1909` - Network accessline 380
{"matched_text": "HYPERTENSION", "from": 23, "to": 34, "match_type": "PREF", "concept_label": "hypertension", "concept_id": "DOID_10763", "concept_full_id": "http://purl.obolibrary.org/obo/DOID_10763"}, - Network accessline 381
"concept_label": "hypertension", "concept_id": "DOID_10763", "concept_full_id": "http://purl.obolibrary.org/obo/DOID_10763"}, {"matched_text": "TYPE 2 DIABETES MELLITUS", "from": 40, "to": 63, "match_type": "PREF", - Network accessline 382
"concept_full_id": "http://purl.obolibrary.org/obo/DOID_10763"}, - Network accessline 385
{"matched_text": "METFORMIN", "from": 77, "to": 85, "match_type": "PREF", "concept_label": "metformin", "concept_id": "6809", "concept_full_id": "http://purl.bioontology.org/ontology/RXNORM/6809"} - Network accessline 386
"concept_label": "metformin", "concept_id": "6809", "concept_full_id": "http://purl.bioontology.org/ontology/RXNORM/6809"} ], "metadata": {"source": "BioPortal Annotator (NCBO)", "total_annotations": 3, "text_length": 86}} - Network accessline 387
"concept_full_id": "http://purl.bioontology.org/ontology/RXNORM/6809"} - Network accessline 404
or a full FHIR system URI), `code` (required). `tu test` PASS, 2/2: `{"system": "snomed", "code": "22298006"}` -> `{"code": "22298006", "display": "Myocardial infarction", "system": "http://snomed.info/sct"}`; - Network accessline 405
`{"system": "snomed", "code": "22298006"}` -> `{"code": "22298006", "display": "Myocardial infarction", "system": "http://snomed.info/sct"}`; `{"system": "snomed", "code": "73211009"}` -> `"Diabetes mellitus"`. - Network accessline 406
"display": "Myocardial infarction", "system": "http://snomed.info/sct"}`; - Network accessline 408
- **`FHIRTerminology_expand_valueset`** — `value_set_url` (required), `limit` (int, 1-500, default 50). `tu test` PASS, 2/2. **Custom call** `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006", "limit": 5}` - Network accessline 409
`limit` (int, 1-500, default 50). `tu test` PASS, 2/2. **Custom call** `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006", "limit": 5}` -> real subtype list: `1755008` ("Old myocardial infarction"), - Network accessline 410
`{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006", "limit": 5}` - Network accessline 411
-> real subtype list: `1755008` ("Old myocardial infarction"), `15990001` ("Acute myocardial infarction of posterolateral wall"), plus more, all sharing `"system": "http://snomed.info/sct"`. Use the - Network accessline 412
`15990001` ("Acute myocardial infarction of posterolateral wall"), plus more, all sharing `"system": "http://snomed.info/sct"`. Use the `isa/<code>` value-set URL pattern for "every descendant of this SNOMED - Network accessline 413
more, all sharing `"system": "http://snomed.info/sct"`. Use the
plugin/skills/tooluniverse-clinical-trial-design/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-clinical-trial-design/SKILL.md
- Network accessline 233
[Credential-related source omitted] - Network accessline 234
[Credential-related source omitted] - Network accessline 235
resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json() - Network accessline 252
# FDA drug approval history drug = "pembrolizumab" fda = requests.get(f"https://api.fda.gov/drug/drugsfda.json?search=openfda.brand_name:{drug}&limit=10").json() - Network accessline 254
fda = requests.get(f"https://api.fda.gov/drug/drugsfda.json?search=openfda.brand_name:{drug}&limit=10").json()
plugin/skills/tooluniverse-clinical-trial-design/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-clinical-trial-design/trial_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-clinical-trial-matching/QUICK_START.md
- Network accessline 136
- Trial availability changes frequently; always verify current status at [ClinicalTrials.gov](https://clinicaltrials.gov)
plugin/skills/tooluniverse-clinical-trial-matching/REPORT_TEMPLATE.md
- Network accessline 178
This report is for informational and research purposes only. Clinical trial eligibility is ultimately determined by the trial investigators based on complete medical records. Patients should discuss all options with their healthcare team. T
plugin/skills/tooluniverse-clinical-trial-protocol/scripts/sample_size_calculator.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-comparative-genomics/scripts/dnds.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-computational-biophysics/scripts/burn_fluids.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-computational-biophysics/scripts/env_risk_assessment.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-computational-biophysics/scripts/enzyme_kinetics.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-computational-biophysics/scripts/epidemiology.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-computational-biophysics/scripts/fluid_calculations.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-computational-biophysics/scripts/herd_immunity.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-computational-biophysics/scripts/iv_drip_rate.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-computational-biophysics/scripts/mc_analyzer.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-computational-biophysics/scripts/radioactive_decay.py
plugin/skills/tooluniverse-connect/SKILL.md
- Network accessline 14
| What you see | The user is on | |---|---| | ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which - Network accessline 15
|---|---| | ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which still works) | **ToolUniverse Con - Network accessline 16
| ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which still works) | **ToolUniverse Connect** (ho - Network accessline 36
The user needs a free account: https://connect.aiscientist.tools/register. Its **Your AI scientist** page (https://connect.aiscientist.tools/connect-agent) shows these steps for each app. - Network accessline 40
- **Claude (claude.ai or the desktop app):** **Customize → Connectors**, click **+ Add**, then **Add custom connector**. Name it ToolUniverse, URL `https://mcp.aiscientist.tools`. Sign in and click **Allow**. In each new chat, click **+** u - Network accessline 41
- **Claude Code:** run `claude mcp add --transport http --scope user tooluniverse "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- claude-code && claude mcp login tooluniverse` A brows - Dangerous shell operationline 42
`claude mcp add --transport http --scope user tooluniverse "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- claude-code && claude mcp login tooluniverse` - Network accessline 42
`claude mcp add --transport http --scope user tooluniverse "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- claude-code && claude mcp login tooluniverse` - Network accessline 43
A browser page opens; the user signs in and clicks **Allow**. Then start a new Claude Code session. - **Codex:** run `codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills. - Network accessline 44
- **Codex:** run `codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- codex` It starts the browser sign-in; if no page opens, run `codex mcp login tooluni - Dangerous shell operationline 45
`codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- codex` - Network accessline 45
`codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- codex` - Network accessline 58
[Credential-related source omitted] - Access to secretsline 58
[Credential-related source omitted] - Access to secretsline 59
[Credential-related source omitted] - Network accessline 77
- **No ToolUniverse tools in this chat:** the app is not connected, or in Claude the connector is off for this chat. Follow section 3. - **Asked to sign in again, or "unauthorized":** Claude Code: `claude mcp login tooluniverse`. Codex: `co - Network accessline 78
- **Asked to sign in again, or "unauthorized":** Claude Code: `claude mcp login tooluniverse`. Codex: `codex mcp login tooluniverse`. Claude: **Customize → Connectors**, open ToolUniverse and connect again. A password reset signs out every - Network accessline 79
- **"Starting" or "unavailable" right after an update:** the service is restarting. Wait a minute and try again. Live status: https://connect.aiscientist.tools/status. - Network accessline 84
Privacy, in one line if asked: the user's conversation stays with their AI app's provider; Connect receives only the tool requests the AI app sends. Details: https://connect.aiscientist.tools/privacy.
plugin/skills/tooluniverse-crispr-screen-analysis/scripts/reference_gene_sets.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-custom-tool/SKILL.md
- Network accessline 41
"type": "BaseRESTTool", "fields": { "endpoint": "https://my-api.example.com/search" - Network accessline 42
"fields": { "endpoint": "https://my-api.example.com/search" }, - Network accessline 43
"endpoint": "https://my-api.example.com/search" - Network accessline 87
def run(self, q: str, limit: int = 10) -> dict: import requests resp = requests.get( - Network accessline 88
import requests resp = requests.get( "https://my-api.example.com/search", - Network accessline 89
resp = requests.get( - Network accessline 90
"https://my-api.example.com/search", - Network accessline 188
return {"error": "query parameter is required"} try: resp = requests.get( - Network accessline 189
try: resp = requests.get( "https://my-api.example.com/search", - Network accessline 190
resp = requests.get( - Network accessline 191
"https://my-api.example.com/search",
plugin/skills/tooluniverse-custom-tool/references/json-tool.md
- Network accessline 9
"description": "...", "type": "BaseRESTTool", "fields": { "endpoint": "https://api.example.com/search" }, - Network accessline 10
"type": "BaseRESTTool", "fields": { "endpoint": "https://api.example.com/search" }, "parameter": { - Network accessline 11
"fields": { "endpoint": "https://api.example.com/search" }, - Network accessline 71
"type": "BaseRESTTool", "fields": { "endpoint": "https://api.example.com/jobs", - Network accessline 72
"fields": { "endpoint": "https://api.example.com/jobs", "method": "POST", - Network accessline 73
"endpoint": "https://api.example.com/jobs", - Network accessline 97
"fields": { "endpoint": "https://api.example.com/search", - Network accessline 98
"endpoint": "https://api.example.com/search", - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 111
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Network accessline 114
"domain": "Drugs & Chemistry", "type": "secret", "register_url": "https://example.com/get-a-key", - Network accessline 115
"type": "secret", "register_url": "https://example.com/get-a-key", "purpose": "What this key unlocks (one sentence).", - Network accessline 116
"register_url": "https://example.com/get-a-key", - Access to secretsline 135
[Credential-related source omitted] - Network accessline 215
import urllib.request, json with urllib.request.urlopen("https://api.example.com/search?q=test") as r: - Network accessline 216
with urllib.request.urlopen("https://api.example.com/search?q=test") as r: - Network accessline 225
**Verify the URL is a real JSON endpoint.** Some URLs that look like REST APIs (e.g., `https://certification.example.org/api/projects`) may redirect to a static HTML page. A urllib fetch will show you the Content-Type and body immediately, - Network accessline 226
(e.g., `https://certification.example.org/api/projects`) may redirect to a static HTML page.
plugin/skills/tooluniverse-custom-tool/references/python-tool.md
- Network accessline 27
def run(self, q: str, limit: int = 10) -> dict: import requests resp = requests.get( - Network accessline 28
import requests resp = requests.get( "https://my-api.example.com/search", - Network accessline 29
resp = requests.get( - Network accessline 30
"https://my-api.example.com/search", - Network accessline 68
return {"error": "query parameter is required"} try: resp = requests.get( - Network accessline 69
try: resp = requests.get( "https://my-api.example.com/search", - Network accessline 70
resp = requests.get( - Network accessline 71
"https://my-api.example.com/search", - Network accessline 223
if _cache is None: import requests resp = requests.get("https://api.example.com/full-index", timeout=timeout) - Network accessline 224
import requests resp = requests.get("https://api.example.com/full-index", timeout=timeout) resp.raise_for_status() - Network accessline 225
resp = requests.get("https://api.example.com/full-index", timeout=timeout) - Access to secretsline 248
[Credential-related source omitted] - Access to secretsline 249
[Credential-related source omitted] - Access to secretsline 250
[Credential-related source omitted] - Network accessline 299
import requests try: resp = requests.get("https://api.example.com/search", params={"q": q}, timeout=30) - Network accessline 300
try: resp = requests.get("https://api.example.com/search", params={"q": q}, timeout=30) resp.raise_for_status() - Network accessline 301
resp = requests.get("https://api.example.com/search", params={"q": q}, timeout=30)
plugin/skills/tooluniverse-data-wrangling/SKILL.md
- Network accessline 62
import pysam # BAM (requires pysam) bam = pysam.AlignmentFile("aligned.bam", "rb") for read in bam.fetch("chr1", 1000, 2000): print(read.query_name) - Network accessline 64
for read in bam.fetch("chr1", 1000, 2000): print(read.query_name) - Network accessline 164
import requests base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 165
base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" # Search -> get IDs -> fetch records in batches ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA1+AND+human&retmax=500&retmode=json").json() - Network accessline 166
# Search -> get IDs -> fetch records in batches ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA1+AND+human&retmax=500&retmode=json").json() id_list = ids["esearchresult"]["idlist"] - Network accessline 167
ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA1+AND+human&retmax=500&retmode=json").json() - Network accessline 170
for i in range(0, len(id_list), 500): batch = ",".join(id_list[i:i+500]) data = requests.get(f"{base}/efetch.fcgi?db=gene&id={batch}&retmode=xml").text - Network accessline 172
data = requests.get(f"{base}/efetch.fcgi?db=gene&id={batch}&retmode=xml").text - Network accessline 178
# UniProt bulk TSV download with cursor pagination url = "https://rest.uniprot.org/uniprotkb/search?query=organism_id:9606+AND+keyword:kinase&format=tsv&size=500" - Network accessline 179
url = "https://rest.uniprot.org/uniprotkb/search?query=organism_id:9606+AND+keyword:kinase&format=tsv&size=500" - Network accessline 180
all_rows = [] while url: resp = requests.get(url) - Network accessline 181
while url: resp = requests.get(url) all_rows.append(resp.text) - Network accessline 182
resp = requests.get(url) - Network accessline 193
{"op":"=","content":{"field":"demographic.vital_status","value":"Dead"}} ]} cases = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 194
]} cases = requests.post("https://api.gdc.cancer.gov/cases", json={ "filters": filters, "fields": "demographic.vital_status,diagnoses.days_to_death", - Network accessline 195
cases = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 204
# Direct NHANES XPT download (any cycle, any component) cycle, component = "2017-2018", "DEMO_J" url = f"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/{component}.XPT" - Network accessline 205
cycle, component = "2017-2018", "DEMO_J" url = f"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/{component}.XPT" df = pd.read_sas(io.BytesIO(requests.get(url).content), format="xport") - Network accessline 206
url = f"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/{component}.XPT" - Network accessline 207
df = pd.read_sas(io.BytesIO(requests.get(url).content), format="xport") - Network accessline 213
# GWAS Catalog full download (37MB TSV, all associations) url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 214
url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 223
# PubChem batch property retrieval (up to 100 CIDs at once) cids = "2244,5988,3672" # aspirin, sucrose, ibuprofen url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA/JSON" - Network accessline 224
cids = "2244,5988,3672" # aspirin, sucrose, ibuprofen url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA/JSON" props = requests.get(url).json()["PropertyTable"]["Properties"] - Network accessline 225
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA/JSON" - Network accessline 226
props = requests.get(url).json()["PropertyTable"]["Properties"] - Network accessline 232
# GEO series matrix direct download geo_id = "GSE12345" url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{geo_id[:5]}nnn/{geo_id}/matrix/{geo_id}_series_matrix.txt.gz" - Network accessline 233
geo_id = "GSE12345" url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{geo_id[:5]}nnn/{geo_id}/matrix/{geo_id}_series_matrix.txt.gz" df = pd.read_csv(url, sep="\t", comment="!", index_col=0) - Network accessline 234
url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{geo_id[:5]}nnn/{geo_id}/matrix/{geo_id}_series_matrix.txt.gz" - Network accessline 237
# GTEx bulk expression (median TPM per tissue) url = "https://storage.googleapis.com/adult-gtex/bulk-gex/v8/rna-seq/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.gz" - Network accessline 238
url = "https://storage.googleapis.com/adult-gtex/bulk-gex/v8/rna-seq/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.gz" - Network accessline 249
[Credential-related source omitted] - Network accessline 250
[Credential-related source omitted] - Network accessline 251
resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json() - Network accessline 262
all_results = [] while cursor: resp = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 263
while cursor: resp = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", params={"query": "BRCA1 AND resistance", "format": "json", "pageSize": 100, "cursorMark": cursor}).json() - Network accessline 264
resp = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 273
# Zenodo: search + download files record = requests.get("https://zenodo.org/api/records", params={"q": "proteomics cancer", "size": 5}).json()["hits"]["hits"][0] - Network accessline 274
record = requests.get("https://zenodo.org/api/records", params={"q": "proteomics cancer", "size": 5}).json()["hits"]["hits"][0] - Network accessline 276
content = requests.get(f["links"]["self"]).content # download each file - Network accessline 335
offset = 0 while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30).json() - Network accessline 336
while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30).json() batch = resp.get("data", resp.get("results", resp.get("hits", []))) - Network accessline 337
resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30).json() - Network accessline 346
[Credential-related source omitted] - Network accessline 347
[Credential-related source omitted] - Network accessline 348
resp = requests.get(api_url, params=params).json() - Network accessline 357
def fetch_with_retry(url, max_retries=3, **kwargs): for attempt in range(max_retries): resp = requests.get(url, timeout=30, **kwargs) - Network accessline 358
for attempt in range(max_retries): resp = requests.get(url, timeout=30, **kwargs) if resp.status_code == 200: return resp - Network accessline 359
resp = requests.get(url, timeout=30, **kwargs) - Access to secretsline 373
[Credential-related source omitted] - Access to secretsline 375
[Credential-related source omitted] - Network accessline 381
def download_large_file(url, output_path): with requests.get(url, stream=True, timeout=300) as r: - Network accessline 382
with requests.get(url, stream=True, timeout=300) as r: - Network accessline 391
resp = requests.get(url, timeout=30)
plugin/skills/tooluniverse-data-wrangling/references/specialized-domains.md
- Network accessline 26
# PRIDE project files — search then download raw/processed data project = requests.get("https://www.ebi.ac.uk/pride/ws/archive/v2/projects/PXD012345").json() - Network accessline 27
project = requests.get("https://www.ebi.ac.uk/pride/ws/archive/v2/projects/PXD012345").json() - Network accessline 28
files = requests.get(f"https://www.ebi.ac.uk/pride/ws/archive/v2/projects/PXD012345/files").json() - Network accessline 33
# ProteomeXchange: search across PRIDE + MassIVE + jPOST px = requests.get("https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD012345&outputMode=JSON").json() - Network accessline 34
px = requests.get("https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD012345&outputMode=JSON").json() - Network accessline 40
# MetaboLights study download study_id = "MTBLS1234" study = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}").json() - Network accessline 41
study_id = "MTBLS1234" study = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}").json() # Download metabolite assignment file - Network accessline 42
study = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}").json() - Network accessline 44
files = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}/files").json() - Network accessline 46
# Metabolomics Workbench REST mw = requests.get("https://www.metabolomicsworkbench.org/rest/study/study_id/ST001234/analysis").json() - Network accessline 47
mw = requests.get("https://www.metabolomicsworkbench.org/rest/study/study_id/ST001234/analysis").json() - Network accessline 49
# HMDB metabolite data hmdb = requests.get("https://hmdb.ca/metabolites/HMDB0000001.xml").text # XML format - Network accessline 50
hmdb = requests.get("https://hmdb.ca/metabolites/HMDB0000001.xml").text # XML format - Network accessline 56
# MGnify: search analyses, download taxonomy/function profiles analyses = requests.get("https://www.ebi.ac.uk/metagenomics/api/v1/analyses", - Network accessline 57
analyses = requests.get("https://www.ebi.ac.uk/metagenomics/api/v1/analyses", - Network accessline 59
# Download OTU/taxonomy TSV for a in analyses["data"]: tax_url = f"https://www.ebi.ac.uk/metagenomics/api/v1/analyses/{a['id']}/downloads" - Network accessline 60
for a in analyses["data"]: tax_url = f"https://www.ebi.ac.uk/metagenomics/api/v1/analyses/{a['id']}/downloads" downloads = requests.get(tax_url).json() - Network accessline 61
tax_url = f"https://www.ebi.ac.uk/metagenomics/api/v1/analyses/{a['id']}/downloads" - Network accessline 62
downloads = requests.get(tax_url).json() - Network accessline 64
# GMREPO: gut microbiome phenotype associations gmrepo = requests.get("https://gmrepo.humangut.info/api/getAssociatedSpeciesByMeshID", - Network accessline 65
gmrepo = requests.get("https://gmrepo.humangut.info/api/getAssociatedSpeciesByMeshID", - Network accessline 74
offset = 0 while True: resp = requests.get("https://api.gbif.org/v1/occurrence/search", - Network accessline 75
while True: resp = requests.get("https://api.gbif.org/v1/occurrence/search", params={"scientificName": "Panthera tigris", "limit": 300, "offset": offset}).json() - Network accessline 76
resp = requests.get("https://api.gbif.org/v1/occurrence/search", - Network accessline 82
# iNaturalist observations obs = requests.get("https://api.inaturalist.org/v1/observations", - Network accessline 83
obs = requests.get("https://api.inaturalist.org/v1/observations", - Network accessline 90
# FlyBase gene data fb = requests.get("https://api.flybase.org/api/v1.0/gene/FBgn0000490").json() # dpp gene - Network accessline 91
fb = requests.get("https://api.flybase.org/api/v1.0/gene/FBgn0000490").json() # dpp gene - Network accessline 93
# WormBase gene info wb = requests.get("https://wormbase.org/rest/widget/gene/WBGene00006763/overview", - Network accessline 94
wb = requests.get("https://wormbase.org/rest/widget/gene/WBGene00006763/overview", - Network accessline 97
# ZFIN (zebrafish) gene expression zfin = requests.get("https://zfin.org/action/api/marker/ZDB-GENE-980526-166/expression").json() - Network accessline 98
zfin = requests.get("https://zfin.org/action/api/marker/ZDB-GENE-980526-166/expression").json() - Network accessline 100
# RGD (rat) — disease annotations for a gene rgd = requests.get("https://rest.rgd.mcw.edu/rgdws/genes/Tp53/9606").json() # human TP53 - Network accessline 101
rgd = requests.get("https://rest.rgd.mcw.edu/rgdws/genes/Tp53/9606").json() # human TP53 - Network accessline 107
# Reactome pathway participants pathway_id = "R-HSA-109582" # Hemostasis participants = requests.get(f"https://reactome.org/ContentService/data/participants/{pathway_id}").json() - Network accessline 109
participants = requests.get(f"https://reactome.org/ContentService/data/participants/{pathway_id}").json() - Network accessline 111
# STRING protein-protein interactions (bulk) proteins = "9606.ENSP00000269305%0d9606.ENSP00000344818" # TP53, MDM2 network = requests.get(f"https://string-db.org/api/json/network?identifiers={proteins}&species=9606").json() - Network accessline 113
network = requests.get(f"https://string-db.org/api/json/network?identifiers={proteins}&species=9606").json() - Network accessline 115
# BioGRID interactions for a gene (tab-delimited bulk) url = "https://webservice.thebiogrid.org/interactions/?searchNames=true&geneList=BRCA1&taxId=9606&format=json&accesskey=YOUR_KEY" - Network accessline 116
url = "https://webservice.thebiogrid.org/interactions/?searchNames=true&geneList=BRCA1&taxId=9606&format=json&accesskey=YOUR_KEY" - Network accessline 118
# WikiPathways: download pathway as GPML or GMT gpml = requests.get("https://www.wikipathways.org/wikipathways/wpi/wpi.php?action=downloadFile&type=gpml&pwTitle=Pathway:WP254").text - Network accessline 119
gpml = requests.get("https://www.wikipathways.org/wikipathways/wpi/wpi.php?action=downloadFile&type=gpml&pwTitle=Pathway:WP254").text - Network accessline 125
# OLS term search + hierarchy traversal terms = requests.get("https://www.ebi.ac.uk/ols4/api/search", - Network accessline 126
terms = requests.get("https://www.ebi.ac.uk/ols4/api/search", - Network accessline 129
# Get children of a GO term children = requests.get("https://www.ebi.ac.uk/ols4/api/ontologies/go/terms/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FGO_0006915/children").json() - Network accessline 130
children = requests.get("https://www.ebi.ac.uk/ols4/api/ontologies/go/terms/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FGO_0006915/children").json() - Network accessline 132
# HPO: phenotype-to-disease annotations hpo = requests.get("https://hpo.jax.org/api/hpo/term/HP:0001250/diseases").json() - Network accessline 133
hpo = requests.get("https://hpo.jax.org/api/hpo/term/HP:0001250/diseases").json() - Network accessline 135
# Gene Ontology annotations bulk (GAF format) # Download from: http://current.geneontology.org/annotations/goa_human.gaf.gz - Network accessline 136
# Download from: http://current.geneontology.org/annotations/goa_human.gaf.gz - Network accessline 142
# IEDB epitope search epitopes = requests.get("https://query-api.iedb.org/epitope_search", - Network accessline 143
epitopes = requests.get("https://query-api.iedb.org/epitope_search", - Network accessline 146
# VDJdb: T-cell receptor specificity database vdjdb = pd.read_csv("https://raw.githubusercontent.com/antigenomics/vdjdb-db/master/latest-version.zip", - Network accessline 147
vdjdb = pd.read_csv("https://raw.githubusercontent.com/antigenomics/vdjdb-db/master/latest-version.zip", - Network accessline 150
# ImmPort shared data (requires registration) # Search at: https://www.immport.org/shared/search - Network accessline 151
# Search at: https://www.immport.org/shared/search - Network accessline 157
# DGIdb drug-gene interactions dgi = requests.get("https://dgidb.org/api/v2/interactions.json", - Network accessline 158
dgi = requests.get("https://dgidb.org/api/v2/interactions.json", - Network accessline 161
# PharmGKB clinical annotations for a gene pgkb = requests.get("https://api.pharmgkb.org/v1/data/clinicalAnnotation", - Network accessline 162
pgkb = requests.get("https://api.pharmgkb.org/v1/data/clinicalAnnotation", - Network accessline 165
# SIDER side effect frequencies (bulk download) # Download from: http://sideeffects.embl.de/media/download/meddra_freq.tsv.gz - Network accessline 166
# Download from: http://sideeffects.embl.de/media/download/meddra_freq.tsv.gz - Network accessline 172
# TCIA: cancer imaging collections collections = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getCollectionValues").json() - Network accessline 173
collections = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getCollectionValues").json() - Network accessline 174
# Get series for a patient series = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getSeries", params={"Collection": "TCGA-BRCA", "PatientID": "TCGA-A1-A0SB"}).json() - Network accessline 175
series = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getSeries", - Network accessline 178
# Human Protein Atlas: tissue expression hpa = requests.get("https://www.proteinatlas.org/api/search_download.php?search=TP53&format=json&columns=g,t,up").json() - Network accessline 179
hpa = requests.get("https://www.proteinatlas.org/api/search_download.php?search=TP53&format=json&columns=g,t,up").json() - Network accessline 181
# Allen Brain Atlas: gene expression in brain regions aba = requests.get("https://api.brain-map.org/api/v2/data/query.json?criteria=model::Gene,rma::criteria,[acronym$eq'BDNF']").json() - Network accessline 182
aba = requests.get("https://api.brain-map.org/api/v2/data/query.json?criteria=model::Gene,rma::criteria,[acronym$eq'BDNF']").json() - Network accessline 188
# Download PDB/CIF structure files directly pdb_id = "1A2B" pdb_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.pdb").text - Network accessline 189
pdb_id = "1A2B" pdb_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.pdb").text cif_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif").text - Network accessline 190
pdb_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.pdb").text - Network accessline 191
cif_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif").text - Network accessline 193
# AlphaFold predicted structure by UniProt ID uniprot_id = "P04637" # TP53 af_pdb = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb").text - Network accessline 194
uniprot_id = "P04637" # TP53 af_pdb = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb").text af_cif = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.cif").text - Network accessline 195
af_pdb = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb").text - Network accessline 196
af_cif = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.cif").text - Network accessline 198
# RCSB advanced search (GraphQL) for bulk queries query = {"query": {"type": "terminal", "service": "text", "parameters": {"attribute": "rcsb_entity_source_organism.taxonomy_lineage.name", "operator": "exact_match", "value": "Homo sapiens"} - Network accessline 200
results = requests.post("https://search.rcsb.org/rcsbsearch/v2/query", json=query).json() - Network accessline 206
# ClinVar bulk download (variant_summary, ~200MB) # df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") - Network accessline 207
# df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") - Network accessline 209
# CIViC GraphQL API — all evidence items for a gene query = '{"query": "{ gene(entrezId: 673) { name variants { nodes { name evidenceItems { nodes { description evidenceLevel } } } } } }"}' civic = requests.post("https://civicdb.org/api/gra - Network accessline 211
civic = requests.post("https://civicdb.org/api/graphql", json={"query": query}).json() - Network accessline 213
# ClinGen allele registry allele = requests.get("https://reg.clinicalgenome.org/allele?hgvs=NM_000059.4:c.68_69del").json() - Network accessline 214
allele = requests.get("https://reg.clinicalgenome.org/allele?hgvs=NM_000059.4:c.68_69del").json() - Network accessline 226
# ARCHS4 gene expression (pre-computed from SRA) archs4 = requests.get("https://maayanlab.cloud/archs4/search/loadExpressionTSV.php", - Network accessline 227
archs4 = requests.get("https://maayanlab.cloud/archs4/search/loadExpressionTSV.php", - Network accessline 230
# Without cellxgene_census: download h5ad directly from cellxgene data portal # Browse collections at https://cellxgene.cziscience.com/collections - Network accessline 231
# Browse collections at https://cellxgene.cziscience.com/collections - Network accessline 237
# CTD: chemical-gene-disease interactions ctd = requests.get("https://ctdbase.org/tools/batchQuery.go", - Network accessline 238
ctd = requests.get("https://ctdbase.org/tools/batchQuery.go", - Network accessline 241
# EPA CompTox Dashboard comptox = requests.get("https://comptox.epa.gov/dashboard/api/search/chemical/equal/Bisphenol%20A").json() - Network accessline 242
comptox = requests.get("https://comptox.epa.gov/dashboard/api/search/chemical/equal/Bisphenol%20A").json()
plugin/skills/tooluniverse-database-logins/SKILL.md
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[Credential-related source omitted] - Network accessline 19
| Login | Unlocks | Where to get it | Cost / wait | |---|---|---|---| | `NCBI_API_KEY` (+ `NCBI_EMAIL`) | Faster PubMed, Gene, Protein and other NCBI tools (they work without it, slower) | https://account.ncbi.nlm.nih.gov/settings/ → API Ke - Network accessline 20
|---|---|---|---| | `NCBI_API_KEY` (+ `NCBI_EMAIL`) | Faster PubMed, Gene, Protein and other NCBI tools (they work without it, slower) | https://account.ncbi.nlm.nih.gov/settings/ → API Key Management | Free, ~2 minutes | | `OMIM_API_KEY` | - Network accessline 21
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| `HF_TOKEN` | Hugging Face–hosted models and datasets | https://huggingface.co/settings/tokens | Free account | - Network accessline 29
The full list, with steps for each, is on https://connect.aiscientist.tools/credentials (search box). Before the user signs up for anything paid, metered, or requiring a license or an institutional claim, say so and let them decide. - Network accessline 39
1. Open https://connect.aiscientist.tools/credentials. - Access to secretsline 44
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plugin/skills/tooluniverse-dataset-discovery/SKILL.md
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# --- Download from URL first, then parse --- resp = requests.get(url, timeout=120) - Network accessline 111
resp = requests.get(url, timeout=120) - Network accessline 129
offset = 0 while True: resp = requests.get(f"{api_url}?offset={offset}&limit=100", timeout=30) - Network accessline 130
while True: resp = requests.get(f"{api_url}?offset={offset}&limit=100", timeout=30) batch = resp.json().get("data", []) - Network accessline 131
resp = requests.get(f"{api_url}?offset={offset}&limit=100", timeout=30)
plugin/skills/tooluniverse-diagnostic-test-evaluation/scripts/roc_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-disease-research/SKILL.md
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"max_results": 3, }) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 78
"max_results": 3, }) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 79
}) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], # "curies": ["MONDO:0004979"], "confidence": "HIGH", "source": "zooma", ...}] - Network accessline 79
}) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], # "curies": ["MONDO:0004979"], "confidence": "HIGH", "source": "zooma", ...}] - Network accessline 80
# -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 80
# -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 86
# Inspect which curated datasources back ZOOMA annotations (for provenance): tu.run_tool("ZOOMA_list_datasources", {}) # -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...] - Network accessline 86
# Inspect which curated datasources back ZOOMA annotations (for provenance): tu.run_tool("ZOOMA_list_datasources", {}) # -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...] - Network accessline 88
# -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...] - Network accessline 88
# -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...]
plugin/skills/tooluniverse-disease-research/TOOLS_REFERENCE.md
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plugin/skills/tooluniverse-dose-response/scripts/fit_dose_response.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-drug-drug-interaction/.env.template
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plugin/skills/tooluniverse-drug-drug-interaction/ddi_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-drug-drug-interaction/ddi_working_example.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-drug-drug-interaction/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-drug-repurposing/REFERENCE.md
- Network accessline 669
- **ToolUniverse Documentation**: https://zitniklab.hms.harvard.edu/ToolUniverse/ - Network accessline 670
- **Tool Catalog**: https://zitniklab.hms.harvard.edu/ToolUniverse/tools/ - Network accessline 671
- **DrugBank**: https://go.drugbank.com/ - Network accessline 672
- **OpenTargets**: https://platform.opentargets.org/ - Network accessline 673
- **ChEMBL**: https://www.ebi.ac.uk/chembl/ - Network accessline 674
- **OpenFDA**: https://open.fda.gov/ - Network accessline 675
- **PubMed**: https://pubmed.ncbi.nlm.nih.gov/
plugin/skills/tooluniverse-drug-repurposing/SKILL.md
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| Disease not found | Try synonyms or EFO ID lookup | | No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key st - Network accessline 280
| Disease not found | Try synonyms or EFO ID lookup | | No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key st - Network accessline 281
| No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key still activating — verify at https://data.uspto.gov/myod - Network accessline 281
| No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key still activating — verify at https://data.uspto.gov/myod - Network accessline 282
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plugin/skills/tooluniverse-drug-research/EXAMPLES.md
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| Identifier | Value | Link | |------------|-------|------| | **PubChem CID** | 4091 | [Link](https://pubchem.ncbi.nlm.nih.gov/compound/4091) | - Network accessline 39
|------------|-------|------| | **PubChem CID** | 4091 | [Link](https://pubchem.ncbi.nlm.nih.gov/compound/4091) | | **ChEMBL ID** | CHEMBL1431 | [Link](https://www.ebi.ac.uk/chembl/compound_report_card/CHEMBL1431/) | - Network accessline 40
| **PubChem CID** | 4091 | [Link](https://pubchem.ncbi.nlm.nih.gov/compound/4091) | - Network accessline 41
| **ChEMBL ID** | CHEMBL1431 | [Link](https://www.ebi.ac.uk/chembl/compound_report_card/CHEMBL1431/) | - Network accessline 42
| **RxNorm CUI** | 6809 | RxNorm | | **PharmGKB ID** | PA450657 | [Link](https://www.pharmgkb.org/chemical/PA450657) | | **DailyMed** | Multiple | FDA approved | - Network accessline 43
| **PharmGKB ID** | PA450657 | [Link](https://www.pharmgkb.org/chemical/PA450657) |
plugin/skills/tooluniverse-drug-synergy/scripts/synergy_reference.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-ecology-biodiversity/SKILL.md
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plugin/skills/tooluniverse-electron-microscopy/SKILL.md
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IDR (Image Data Resource, https://idr.openmicroscopy.org) is the reference repository for *light* microscopy and high-content screening data — distinct from the EM databases above. Use it to find published imaging studies and images by cura
plugin/skills/tooluniverse-enzyme-kinetics/scripts/fit_michaelis_menten.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-epidemiological-analysis/SKILL.md
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# Generic download helper — adapt URL and format to your source def download_and_parse(url, fmt="csv"): r = requests.get(url, timeout=120) - Network accessline 69
def download_and_parse(url, fmt="csv"): r = requests.get(url, timeout=120) content = io.BytesIO(r.content) - Network accessline 70
r = requests.get(url, timeout=120) - Access to secretsline 111
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offset = 0 while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30) - Network accessline 146
while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30) batch = resp.json().get("data", []) - Network accessline 147
resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30)
plugin/skills/tooluniverse-epigenomics/.env.template
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plugin/skills/tooluniverse-epigenomics/scripts/methylation_density.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-fastq-qc/scripts/run_fastq_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-gene-disease-association/SKILL.md
plugin/skills/tooluniverse-gene-enrichment/.env.template
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plugin/skills/tooluniverse-gene-enrichment/SKILL.md
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gseapy documentation: https://gseapy.readthedocs.io/ - Network accessline 498
PANTHER API: http://pantherdb.org/services/oai/pantherdb/ - Network accessline 499
STRING API: https://string-db.org/cgi/help?sessionId=&subpage=api - Network accessline 500
Reactome Analysis: https://reactome.org/AnalysisService/
plugin/skills/tooluniverse-gene-enrichment/references/troubleshooting.md
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1. **Check gseapy documentation**: https://gseapy.readthedocs.io/
plugin/skills/tooluniverse-gene-enrichment/scripts/condition_enrichment_screen.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-gene-enrichment/scripts/enrichgo_runner.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-gene-enrichment/scripts/format_enrichment_output.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-gene-enrichment/scripts/gseapy_enrichment_runner.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-glycobiology/references/glygen_tool_reference.md
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"species": ["Homo sapiens", "..."], "composition": [ {"name": "N-Acetylhexosamine", "residue": "hexnac", "count": 4, "cid": "...", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/..."}, - Network accessline 26
"composition": [ {"name": "N-Acetylhexosamine", "residue": "hexnac", "count": 4, "cid": "...", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/..."}, {"name": "N-Acetyl-Neuraminic Acid", "residue": "neuac", "count": 2, "cid": - Network accessline 27
{"name": "N-Acetylhexosamine", "residue": "hexnac", "count": 4, "cid": "...", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/..."}, - Network accessline 28
{"name": "N-Acetyl-Neuraminic Acid", "residue": "neuac", "count": 2, "cid": "439197", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/439197"} - Network accessline 159
"glycosylation": ["..."], "snv": ["..."], "species": [{"common_name": "Human", "glygen_name": "Human", "reference_species": "Homo sapiens [9606]", "url": "https://www.uniprot.org/uniprotkb/P14210"}], - Network accessline 160
"snv": ["..."], "species": [{"common_name": "Human", "glygen_name": "Human", "reference_species": "Homo sapiens [9606]", "url": "https://www.uniprot.org/uniprotkb/P14210"}], "all_sites_count": 3, - Network accessline 161
"species": [{"common_name": "Human", "glygen_name": "Human", "reference_species": "Homo sapiens [9606]", "url": "https://www.uniprot.org/uniprotkb/P14210"}],
plugin/skills/tooluniverse-gpcr-structural-pharmacology/SKILL.md
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- GPCRdb: https://gpcrdb.org — standardized GPCR data with generic numbering - Network accessline 294
- SAbDab: https://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab — structural antibody database - Network accessline 295
- PDBePISA: https://www.ebi.ac.uk/pdbe/pisa — protein interface analysis
plugin/skills/tooluniverse-gwas-drug-discovery/QUICK_START.md
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# Or from source git clone https://github.com/example/tooluniverse.git - Network accessline 31
git clone https://github.com/example/tooluniverse.git - Network accessline 121
// Initialize MCP client const client = new MCPClient({ serverUrl: "http://localhost:3000/tooluniverse" - Network accessline 122
const client = new MCPClient({ serverUrl: "http://localhost:3000/tooluniverse" }); - Network accessline 123
serverUrl: "http://localhost:3000/tooluniverse" - Network accessline 1021
- [GWAS Catalog Documentation](https://www.ebi.ac.uk/gwas/docs) - Network accessline 1022
- [Open Targets Platform](https://platform.opentargets.org/) - Network accessline 1023
- [ChEMBL Database](https://www.ebi.ac.uk/chembl/) - Network accessline 1024
- [ToolUniverse Documentation](https://docs.tooluniverse.org)
plugin/skills/tooluniverse-gwas-drug-discovery/python_implementation.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-gwas-finemapping/.env.template
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plugin/skills/tooluniverse-gwas-finemapping/SKILL.md
- Network accessline 123
**Where to get the raw inputs**: these three tools compute on arrays you supply — they do not fetch summary statistics themselves. `src/tooluniverse/data/gwas_sumstats_tools.json`'s 3 tools (`GWASSumStats_list_studies`, `GWASSumStats_get_tr
plugin/skills/tooluniverse-gwas-finemapping/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-gwas-snp-interpretation/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-gwas-snp-interpretation/SKILL.md
plugin/skills/tooluniverse-gwas-snp-interpretation/python_implementation.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-gwas-study-explorer/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-gwas-study-explorer/python_implementation.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-gwas-trait-to-gene/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-gwas-trait-to-gene/SKILL.md
- Network accessline 224
# Download full GWAS Catalog (all associations, ~37MB TSV) url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 225
url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 231
# Per-study associations via REST study_id = "GCST001234" assocs = requests.get(f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations").json() - Network accessline 233
assocs = requests.get(f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations").json() - Network accessline 274
**GWAS Catalog** - Curator: EBI and NHGRI - URL: https://www.ebi.ac.uk/gwas/ - Network accessline 275
- Curator: EBI and NHGRI - URL: https://www.ebi.ac.uk/gwas/ - Coverage: 100,000+ publications, 500,000+ associations - Network accessline 276
- URL: https://www.ebi.ac.uk/gwas/ - Network accessline 280
**Open Targets Genetics** - Curator: Open Targets consortium - URL: https://genetics.opentargets.org/ - Network accessline 281
- Curator: Open Targets consortium - URL: https://genetics.opentargets.org/ - Coverage: Fine-mapped GWAS, L2G predictions, QTL colocalization - Network accessline 282
- URL: https://genetics.opentargets.org/
plugin/skills/tooluniverse-gwas-trait-to-gene/python_implementation.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-image-analysis/scripts/batch_process.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-image-analysis/scripts/measure_fluorescence.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-image-analysis/scripts/segment_cells.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-immune-repertoire-analysis/ANALYSIS_DETAILS.md
plugin/skills/tooluniverse-immunotherapy-response-prediction/SKILL.md
- Network accessline 201
- OpenTargets: https://platform.opentargets.org - Network accessline 202
- CIViC: https://civicdb.org - Network accessline 203
- FDA Drug Labels: https://dailymed.nlm.nih.gov - Network accessline 204
- DrugBank: https://go.drugbank.com - Network accessline 205
- PubMed: https://pubmed.ncbi.nlm.nih.gov - Network accessline 206
- IEDB: https://www.iedb.org - Network accessline 207
- HPA: https://www.proteinatlas.org - Network accessline 208
- cBioPortal: https://www.cbioportal.org
plugin/skills/tooluniverse-infectious-disease/CHECKLIST.md
- Access to secretsline 33
[Credential-related source omitted]
plugin/skills/tooluniverse-infectious-disease/SKILL.md
plugin/skills/tooluniverse-infectious-disease/TOOLS_REFERENCE.md
plugin/skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-install-skills/SKILL.md
- Network accessline 33
# 1. Download skills from GitHub (shallow, sparse — only skills/ folder) git clone --depth 1 --filter=blob:none --sparse \ https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Network accessline 34
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Network accessline 35
https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 49
rm -rf /tmp/tu-skills
plugin/skills/tooluniverse-lab-data-standardization/references/schema_overview.md
- Network accessline 10
"tier": "native" | "fallback", "note": "<present only for tier=fallback: 'not ASM-validated, best-effort structural parse'>", "manifest": "http://purl.allotrope.org/... (Tier 1 only)", - Network accessline 11
"note": "<present only for tier=fallback: 'not ASM-validated, best-effort structural parse'>", "manifest": "http://purl.allotrope.org/... (Tier 1 only)", "source_file": "plate_reader_run1.csv", - Network accessline 12
"manifest": "http://purl.allotrope.org/... (Tier 1 only)",
plugin/skills/tooluniverse-lab-data-standardization/scripts/convert_instrument_data.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-lab-data-standardization/scripts/flatten_to_csv.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-lab-data-standardization/scripts/validate_output.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-lab-protocols/SKILL.md
- Access to secretsline 3
[Credential-related source omitted] - Network accessline 13
[Credential-related source omitted] - Access to secretsline 13
[Credential-related source omitted] - Network accessline 57
- Search (`ProtocolsIO_search_protocols`) uses the v3 API; per-protocol detail and steps use the newer v4 API — this is a real API-versioning quirk in protocols.io itself, not an inconsistency in this tool. - **Live-verified**: an invalid/m - Network accessline 59
- The response-body schemas here (protocol fields, step structure) were built directly from protocols.io's published API documentation (https://apidoc.protocols.io/), not live-verified end-to-end in this environment (no real API key was ava
plugin/skills/tooluniverse-literature-deep-research/EXAMPLES.md
- Network accessline 439
"citation_count": 612, "oa_status": "gold", "oa_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704954/" - Network accessline 440
"oa_status": "gold", "oa_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704954/" } - Network accessline 441
"oa_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704954/"
plugin/skills/tooluniverse-literature-deep-research/FULLTEXT_STRATEGY.md
- Network accessline 84
get_webpage_text_from_url(url="https://doi.org/10.1016/...")
plugin/skills/tooluniverse-literature-deep-research/REPORT_TEMPLATE.md
plugin/skills/tooluniverse-literature-deep-research/SKILL.md
- Network accessline 167
**Citation cross-references — naming collision, read carefully**: `src/tooluniverse/data/europepmc_citations_tools.json` defines `EPMC_get_citations`, `EPMC_get_references`, and `EuropePMC_get_article_datalinks` — do NOT confuse these with - Access to secretsline 171
[Credential-related source omitted]
plugin/skills/tooluniverse-literature-deep-research/TOOL_NAMES_REFERENCE.md
- Access to secretsline 63
[Credential-related source omitted]
plugin/skills/tooluniverse-medical-imaging-radiology/SKILL.md
- Network accessline 23
what imaging exists — not the images themselves. If the user wants the actual pixels, tell them the DICOM download UI/API is a separate TCIA service (`https://www.cancerimagingarchive.net/`) outside this tool set. - Network accessline 25
service (`https://www.cancerimagingarchive.net/`) outside this tool set.
plugin/skills/tooluniverse-medical-imaging-radiology/references/tcia_tool_reference.md
- Network accessline 3
All 10 tools live-tested against the real TCIA API (`https://services.cancerimagingarchive.net/nbia-api/`) on 2026-09-17. - Network accessline 4
(`https://services.cancerimagingarchive.net/nbia-api/`) on 2026-09-17. - Network accessline 97
"ImageCount": 2, "FileSize": 16357620, "LicenseName": "Creative Commons Attribution 3.0 Unported License", "LicenseURI": "http://creativecommons.org/licenses/by/3.0/", - Network accessline 98
"LicenseName": "Creative Commons Attribution 3.0 Unported License", "LicenseURI": "http://creativecommons.org/licenses/by/3.0/", "CollectionURI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX"} - Network accessline 99
"LicenseURI": "http://creativecommons.org/licenses/by/3.0/", - Network accessline 100
"CollectionURI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX"} - Network accessline 172
{"Series UID": "1.3.6.1.4.1.14519.5.2.1.6279.6001.141365756818074696859567662357", "Collection": "LIDC-IDRI", "Data Description URI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX", - Network accessline 173
"Collection": "LIDC-IDRI", "Data Description URI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX", "Subject ID": "LIDC-IDRI-0001", - Network accessline 174
"Data Description URI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX",
plugin/skills/tooluniverse-meta-analysis/scripts/meta_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-metabolomics-analysis/SKILL.md
- Network accessline 206
**Methods**: - MetaboAnalyst: https://doi.org/10.1093/nar/gkab382 - Network accessline 207
- MetaboAnalyst: https://doi.org/10.1093/nar/gkab382 - Network accessline 208
- XCMS: https://doi.org/10.1021/ac051437y - Network accessline 209
- MSEA: https://doi.org/10.1186/1471-2105-11-395 - Network accessline 211
**Databases**: - HMDB: https://hmdb.ca - Network accessline 212
- HMDB: https://hmdb.ca - Network accessline 213
- KEGG Compound: https://www.genome.jp/kegg/compound/ - Network accessline 214
- Reactome: https://reactome.org
plugin/skills/tooluniverse-metabolomics/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-metabolomics/QUICK_START.md
- Access to secretsline 415
[Credential-related source omitted] - Access to secretsline 423
[Credential-related source omitted] - Access to secretsline 424
[Credential-related source omitted] - Network accessline 503
- **ToolUniverse Documentation**: https://github.com/mims-harvard/ToolUniverse - Network accessline 504
- **HMDB Database**: https://hmdb.ca - Network accessline 505
- **MetaboLights**: https://www.ebi.ac.uk/metabolights/ - Network accessline 506
- **Metabolomics Workbench**: https://www.metabolomicsworkbench.org - Network accessline 507
- **PubChem**: https://pubchem.ncbi.nlm.nih.gov
plugin/skills/tooluniverse-metabolomics/diabetes_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-metabolomics/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-metabolomics/scripts/metabolism_ref.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-metabolomics/test1_metabolites.md
- Network accessline 13
**Formula**: C6H12O6 **Molecular Weight**: 180.16 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 15
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 20
**Formula**: C3H5O3- **Molecular Weight**: 89.07 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=lactate&searcher=metabolites - Network accessline 22
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=lactate&searcher=metabolites
plugin/skills/tooluniverse-metabolomics/test2_study.md
plugin/skills/tooluniverse-metabolomics/test4_comprehensive.md
- Network accessline 15
**Formula**: C6H12O6 **Molecular Weight**: 180.16 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 17
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 22
**Formula**: C3H3O3- **Molecular Weight**: 87.05 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=pyruvate&searcher=metabolites - Network accessline 24
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=pyruvate&searcher=metabolites - Network accessline 30
**Release Date**: N/A **Modified Time**: 2025-09-02T13:34:14.536288 **HTTP URL**: http://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1 - Network accessline 32
**HTTP URL**: http://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1
plugin/skills/tooluniverse-ml-inference-utility/SKILL.md
- Network accessline 35
1. **Check for `HF_TOKEN` before promising a result.** If it's unset, tell the user plainly that HuggingFace's serverless inference now requires a token, point them to https://huggingface.co/settings/tokens to create a free one - Network accessline 36
user plainly that HuggingFace's serverless inference now requires a token, point them to https://huggingface.co/settings/tokens to create a free one (a `read`-scope token is sufficient), and stop rather than guessing an output. - Network accessline 37
point them to https://huggingface.co/settings/tokens to create a free one - Network accessline 220
Both **require `REPLICATE_API_TOKEN`** (register at https://replicate.com/account/api-tokens) — **live-verified**: with no token - Network accessline 221
https://replicate.com/account/api-tokens) — **live-verified**: with no token
plugin/skills/tooluniverse-ml-inference-utility/references/hf_inference_tool_reference.md
plugin/skills/tooluniverse-molecular-cloning/scripts/cloning_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-multi-omics-integration/SKILL.md
plugin/skills/tooluniverse-multiomic-disease-characterization/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-network-pharmacology/scripts/network_proximity.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 36
STRING_LINKS = "https://stringdb-downloads.org/download/protein.links.v12.0/9606.protein.links.v12.0.txt.gz" - Network accessline 37
STRING_INFO = "https://stringdb-downloads.org/download/protein.info.v12.0/9606.protein.info.v12.0.txt.gz" - Network accessline 45
if not os.path.exists(path) or os.path.getsize(path) == 0: sys.stderr.write(f"Downloading {name} (one-time)...\n") urllib.request.urlretrieve(url, path) - Network accessline 46
sys.stderr.write(f"Downloading {name} (one-time)...\n") urllib.request.urlretrieve(url, path) return path - Network accessline 47
urllib.request.urlretrieve(url, path)
plugin/skills/tooluniverse-neurosynth-meta-analysis/SKILL.md
- Network accessline 74
- `references/method.md` — the statistical method in more detail, data provenance, and known limitations - Neurosynth: https://neurosynth.org - Network accessline 75
- Neurosynth: https://neurosynth.org - Network accessline 76
- Data source: https://github.com/neurosynth/neurosynth-data - Network accessline 77
- For a full ALE/MKDA meta-analysis pipeline instead of this simplified proxy: NiMARE (https://nimare.readthedocs.io)
plugin/skills/tooluniverse-neurosynth-meta-analysis/references/method.md
- Network accessline 5
All data comes from the public `neurosynth/neurosynth-data` GitHub repository (https://github.com/neurosynth/neurosynth-data), maintained by the Neurosynth - Network accessline 6
(https://github.com/neurosynth/neurosynth-data), maintained by the Neurosynth - Network accessline 89
if a fetch 404s. - **For anything publication-grade**: use NiMARE (https://nimare.readthedocs.io), which implements real ALE/MKDA with - Network accessline 90
- **For anything publication-grade**: use NiMARE (https://nimare.readthedocs.io), which implements real ALE/MKDA with proper cluster-level family-wise-error correction against this same - Network accessline 91
(https://nimare.readthedocs.io), which implements real ALE/MKDA with
plugin/skills/tooluniverse-neurosynth-meta-analysis/scripts/_neurosynth_data.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-neurosynth-meta-analysis/scripts/decode_coordinate.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-neurosynth-meta-analysis/scripts/download_data.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 30
RAW_BASE = "https://raw.githubusercontent.com/neurosynth/neurosynth-data/master" - Access to secretsline 41
[Credential-related source omitted] - Network accessline 51
print(f" downloading {url}") try: with urllib.request.urlopen(url, timeout=60) as resp: - Network accessline 52
try: with urllib.request.urlopen(url, timeout=60) as resp: if resp.status != 200: - Network accessline 53
with urllib.request.urlopen(url, timeout=60) as resp: - Network accessline 92
"\nOne or more downloads failed. Do NOT proceed with analysis on " "partial data - re-run this script, or check " "https://github.com/neurosynth/neurosynth-data for the current " - Network accessline 93
"partial data - re-run this script, or check " "https://github.com/neurosynth/neurosynth-data for the current " "file names if the version number has moved on.", - Network accessline 94
"https://github.com/neurosynth/neurosynth-data for the current "
plugin/skills/tooluniverse-neurosynth-meta-analysis/scripts/search_by_term.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-nfcore-pipelines/SKILL.md
- Network accessline 29
3. **Never invent a pipeline version.** nf-core pipelines are versioned and pinned with `-r <version>`. If you are not certain of the current stable release, tell the user to check `https://nf-co.re/<pipeline>/releases` - Network accessline 30
pinned with `-r <version>`. If you are not certain of the current stable release, tell the user to check `https://nf-co.re/<pipeline>/releases` rather than guessing a version number — the version numbers below were - Network accessline 31
release, tell the user to check `https://nf-co.re/<pipeline>/releases` - Network accessline 290
Apache-2.0. This skill orchestrates them but does not vendor or modify pipeline code. Always pin `-r <version>` and verify current releases at https://nf-co.re before a real run — the versions referenced in this skill's - Network accessline 291
pipeline code. Always pin `-r <version>` and verify current releases at https://nf-co.re before a real run — the versions referenced in this skill's reference files may lag behind the latest nf-core release. - Network accessline 292
https://nf-co.re before a real run — the versions referenced in this skill's
plugin/skills/tooluniverse-nfcore-pipelines/references/geo_sra_acquisition.md
- Network accessline 74
curl -L -o sample1.fastq.gz "<direct-url>"
plugin/skills/tooluniverse-nfcore-pipelines/references/pipelines/atacseq.md
plugin/skills/tooluniverse-nfcore-pipelines/references/pipelines/rnaseq.md
plugin/skills/tooluniverse-nfcore-pipelines/references/pipelines/sarek.md
plugin/skills/tooluniverse-nfcore-pipelines/references/troubleshooting.md
plugin/skills/tooluniverse-nfcore-pipelines/scripts/check_environment.py
- Executable scriptline 1
#!/usr/bin/env python3 - Dangerous shell operationline 48
" sudo systemctl start docker\n" - Dangerous shell operationline 50
" sudo usermod -aG docker $USER # then log out and back in\n" - Network accessline 58
return ( "Install Docker Engine (Linux):\n" " curl -fsSL https://get.docker.com | sh\n" - Network accessline 59
"Install Docker Engine (Linux):\n" " curl -fsSL https://get.docker.com | sh\n" " sudo usermod -aG docker $USER # then log out and back in\n" - Dangerous shell operationline 60
" curl -fsSL https://get.docker.com | sh\n" - Network accessline 60
" curl -fsSL https://get.docker.com | sh\n" - Dangerous shell operationline 61
" sudo usermod -aG docker $USER # then log out and back in\n" - Network accessline 61
" sudo usermod -aG docker $USER # then log out and back in\n" "See https://docs.docker.com/engine/install/ for distro-specific steps.\n" "If you cannot get root/sudo (e.g. a locked-down sandbox), Docker cannot " - Network accessline 62
"See https://docs.docker.com/engine/install/ for distro-specific steps.\n" - Dangerous shell operationline 63
"If you cannot get root/sudo (e.g. a locked-down sandbox), Docker cannot " - Network accessline 64
"be installed here — this skill cannot run a real pipeline in that case." ) return "Install Docker Desktop: https://docs.docker.com/get-docker/" - Network accessline 66
return "Install Docker Desktop: https://docs.docker.com/get-docker/" - Network accessline 74
"Nextflow is not installed.", "Install Nextflow:\n" " curl -s https://get.nextflow.io | bash\n" - Network accessline 75
"Install Nextflow:\n" " curl -s https://get.nextflow.io | bash\n" " mkdir -p ~/bin && mv nextflow ~/bin/\n" - Dangerous shell operationline 76
" curl -s https://get.nextflow.io | bash\n" - Network accessline 76
" curl -s https://get.nextflow.io | bash\n" - Dangerous shell operationline 106
" sudo apt-get update && sudo apt-get install -y openjdk-17-jre-headless\n" - Network accessline 106
" sudo apt-get update && sudo apt-get install -y openjdk-17-jre-headless\n" "Other distros: use your package manager to install a JRE >= 11, " "or a user-local install via https://adoptium.net/ if you lack sudo.", - Network accessline 107
"Other distros: use your package manager to install a JRE >= 11, " "or a user-local install via https://adoptium.net/ if you lack sudo.", ) - Network accessline 108
"or a user-local install via https://adoptium.net/ if you lack sudo.",
plugin/skills/tooluniverse-nfcore-pipelines/scripts/detect_data_type.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-nfcore-pipelines/scripts/generate_samplesheet.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-nih-funding-landscape/agents/openai.yaml
plugin/skills/tooluniverse-nih-funding-landscape/references/public-value-cases.md
- Network accessline 326
type-7 transfer `7R01NS121038-02` in FY2022, and continued through `5R01NS121038-05` at UT Southwestern. These are five full project numbers but one distinct core award. `fetch("R01NS121038")` correctly failed because fetch - Network accessline 327
`5R01NS121038-05` at UT Southwestern. These are five full project numbers but one distinct core award. `fetch("R01NS121038")` correctly failed because fetch requires a full project number; either full endpoint record returned a public - Network accessline 328
one distinct core award. `fetch("R01NS121038")` correctly failed because fetch
plugin/skills/tooluniverse-nih-funding-landscape/references/tool-reference.md
- Network accessline 46
search_grants result.pi_profile_id -> get_pi_profile(profile_id) rank_institutions result.entity_id -> get_institution_profile / mechanism_mix / funding_growth search result.id OR search_grants result.project_num -> fetch(id) - Network accessline 47
rank_institutions result.entity_id -> get_institution_profile / mechanism_mix / funding_growth search result.id OR search_grants result.project_num -> fetch(id) search_grants result.project_num -> citation/fetch of one full record - Network accessline 48
search result.id OR search_grants result.project_num -> fetch(id) - Network accessline 289
than one row for one core in a fiscal year. Count the core once when answering “how many awards,” but do not discard legitimate row-level dollars. Fetch one of the full project numbers; `fetch(core_project_num)` returns an actionable - Network accessline 290
“how many awards,” but do not discard legitimate row-level dollars. Fetch one of the full project numbers; `fetch(core_project_num)` returns an actionable error because a core ID is not a citation record. - Network accessline 291
of the full project numbers; `fetch(core_project_num)` returns an actionable - Network accessline 315
1. Validate the full project-number form. 2. Run `search_grants(project_num=...)`. 3. If zero, run `fetch(id=...)` only as a canonical-record check; preserve its error message. - Network accessline 316
2. Run `search_grants(project_num=...)`. 3. If zero, run `fetch(id=...)` only as a canonical-record check; preserve its error message. 4. Read `source_status` and say “not found in this snapshot/query.” - Network accessline 317
3. If zero, run `fetch(id=...)` only as a canonical-record check; preserve its error message.
plugin/skills/tooluniverse-nih-funding-landscape/scripts/verify_live_cases.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 769
"exact award fetch returns public URL", exact_fetch["id"] == "1F31DC023096-01" and exact_fetch["url"].startswith("https://opennih.org/projects/"), - Network accessline 770
exact_fetch["id"] == "1F31DC023096-01" and exact_fetch["url"].startswith("https://opennih.org/projects/"), exact_fetch, - Network accessline 771
and exact_fetch["url"].startswith("https://opennih.org/projects/"),
plugin/skills/tooluniverse-nih-funding-landscape/scripts/verify_live_contract.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-nih-funding-landscape/scripts/verify_output_links.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-noncoding-rna/SKILL.md
- Network accessline 233
# Step 1: Download TargetScan predicted targets (one-time, ~10MB zipped) # URL: https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip - Network accessline 234
# URL: https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip import pandas as pd import zipfile, io, requests - Network accessline 238
url = "https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip" resp = requests.get(url, timeout=60) - Network accessline 239
resp = requests.get(url, timeout=60) - Network accessline 263
# Option 1: Download from miRTarBase bulk export (requires browser download first) # Go to: https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/ - Network accessline 264
# Go to: https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/ - Network accessline 267
# Option 2: Use the GitHub data dump # https://github.com/jorainer/mirtarbase — R package with cached data - Network accessline 268
# https://github.com/jorainer/mirtarbase — R package with cached data
plugin/skills/tooluniverse-nutrition-food-composition/SKILL.md
plugin/skills/tooluniverse-organic-chemistry/SKILL.md
- Access to secretsline 101
[Credential-related source omitted]
plugin/skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-organic-chemistry/scripts/crystal_validator.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-organic-chemistry/scripts/degrees_of_unsaturation.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-organic-chemistry/scripts/molecular_complexity.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-organic-chemistry/scripts/molecular_formula.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-organic-chemistry/scripts/smiles_verifier.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-organic-chemistry/scripts/stereochem_tracker.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-peptide-target-deorphanization/SKILL.md
- Access to secretsline 62
[Credential-related source omitted]
plugin/skills/tooluniverse-peptide-target-deorphanization/references/phases.md
- Access to secretsline 33
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 242
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 298
[Credential-related source omitted] - Access to secretsline 308
[Credential-related source omitted] - Access to secretsline 344
[Credential-related source omitted]
plugin/skills/tooluniverse-peptide-target-deorphanization/scripts/cofold_screen.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 10
[Credential-related source omitted] - Access to secretsline 19
[Credential-related source omitted] - Access to secretsline 160
[Credential-related source omitted] - Access to secretsline 177
[Credential-related source omitted] - Access to secretsline 178
[Credential-related source omitted]
plugin/skills/tooluniverse-peptide-target-deorphanization/scripts/deorphanize_peptide.py
plugin/skills/tooluniverse-pharmacogenomics/SKILL.md
plugin/skills/tooluniverse-pharmacokinetics/scripts/nca_from_csv.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-pharmacovigilance/SKILL.md
- Network accessline 344
- FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers - Network accessline 345
- DailyMed: https://dailymed.nlm.nih.gov - Network accessline 346
- PharmGKB: https://www.pharmgkb.org - Network accessline 347
- ClinicalTrials.gov: https://clinicaltrials.gov - Network accessline 348
- OpenFDA: https://open.fda.gov - Network accessline 349
- KEGG Drug: https://www.genome.jp/kegg/drug
plugin/skills/tooluniverse-phylogenetics/SKILL.md
- Network accessline 646
`references/sequence_alignment.md`, `references/tree_building.md`, `references/parsimony_analysis.md`, `scripts/tree_statistics.py` - PhyKIT: https://jlsteenwyk.com/PhyKIT/ - Network accessline 647
- PhyKIT: https://jlsteenwyk.com/PhyKIT/ - Network accessline 648
- Biopython Phylo: https://biopython.org/wiki/Phylo - Network accessline 649
- DendroPy: https://dendropy.org/
plugin/skills/tooluniverse-phylogenetics/references/troubleshooting.md
- Network accessline 570
- `tree_building.md` - Tree construction methods - `parsimony_analysis.md` - Statistical workflows - PhyKIT documentation: https://jlsteenwyk.com/PhyKIT/ - Network accessline 571
- `parsimony_analysis.md` - Statistical workflows - PhyKIT documentation: https://jlsteenwyk.com/PhyKIT/ - Biopython Phylo tutorial: https://biopython.org/wiki/Phylo - Network accessline 572
- PhyKIT documentation: https://jlsteenwyk.com/PhyKIT/ - Network accessline 573
- Biopython Phylo tutorial: https://biopython.org/wiki/Phylo
plugin/skills/tooluniverse-phylogenetics/scripts/busco_target_orthologs.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-phylogenetics/scripts/format_alignment.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-phylogenetics/scripts/phykit_batch.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-phylogenetics/scripts/scogs_paired_compare.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-phylogenetics/scripts/scogs_phykit_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-phylogenetics/scripts/tree_statistics.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-polygenic-risk-score/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-polygenic-risk-score/check_data.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-polygenic-risk-score/python_implementation.py
plugin/skills/tooluniverse-population-genetics/scripts/popgen_calculator.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-precision-medicine-stratification/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-precision-oncology/scripts/gdsc_drug_response.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 34
GDSC_URLS = { "GDSC2": "https://cog.sanger.ac.uk/cancerrxgene/GDSC_release8.5/GDSC2_fitted_dose_response_27Oct23.xlsx", - Network accessline 35
"GDSC2": "https://cog.sanger.ac.uk/cancerrxgene/GDSC_release8.5/GDSC2_fitted_dose_response_27Oct23.xlsx", - Network accessline 36
"GDSC1": "https://cog.sanger.ac.uk/cancerrxgene/GDSC_release8.5/GDSC1_fitted_dose_response_27Oct23.xlsx", - Network accessline 53
if not os.path.exists(path) or os.path.getsize(path) == 0: sys.stderr.write(f"Downloading {dataset} (~21 MB, one-time)...\n") urllib.request.urlretrieve(GDSC_URLS[dataset], path) - Network accessline 54
sys.stderr.write(f"Downloading {dataset} (~21 MB, one-time)...\n") urllib.request.urlretrieve(GDSC_URLS[dataset], path) return pd.read_excel(path) - Network accessline 55
urllib.request.urlretrieve(GDSC_URLS[dataset], path)
plugin/skills/tooluniverse-primer-design/scripts/primer_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-product-safety-surveillance/SKILL.md
- Network accessline 244
- openFDA: https://open.fda.gov - Network accessline 245
- MAUDE (device adverse events): https://www.fda.gov/medical-devices/mandatory-reporting-requirements-manufacturers-importers-and-device-user-facilities - Network accessline 246
- Device recalls / 510(k): https://www.fda.gov/medical-devices - Network accessline 247
- CAERS (food/supplement/cosmetic): https://www.fda.gov/food/compliance-enforcement/cfsan-adverse-event-reporting-system-caers - Network accessline 248
- Drug shortages: https://www.fda.gov/drugs/drug-shortages - Network accessline 249
- Recall classifications: https://www.fda.gov/safety/industry-guidance-recalls
plugin/skills/tooluniverse-protein-design-campaign/scripts/preflight_submission.py
- Executable scriptline 1
#!/usr/bin/env python3 - Instruction override signalline 31
), "selector_instruction": re.compile( r"ignore previous instructions|system prompt|rank this first|" - Instruction override signalline 32
"selector_instruction": re.compile( r"ignore previous instructions|system prompt|rank this first|" r"select this candidate|you are (?:ChatGPT|Claude)", - Instruction override signalline 33
r"ignore previous instructions|system prompt|rank this first|"
plugin/skills/tooluniverse-protein-interactions/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-protein-interactions/DOMAIN_ANALYSIS.md
plugin/skills/tooluniverse-protein-interactions/PHASE2_DISCOVERIES.md
plugin/skills/tooluniverse-protein-interactions/QUICK_START.md
- Access to secretsline 134
[Credential-related source omitted]
plugin/skills/tooluniverse-protein-interactions/SKILL.md
- Access to secretsline 230
[Credential-related source omitted] - Access to secretsline 230
[Credential-related source omitted] - Access to secretsline 230
[Credential-related source omitted] - Network accessline 235
- STRING: https://string-db.org/ - Network accessline 235
- STRING: https://string-db.org/ - Network accessline 235
- STRING: https://string-db.org/ - Network accessline 236
- BioGRID: https://thebiogrid.org/ (register for free API key) - Network accessline 236
- BioGRID: https://thebiogrid.org/ (register for free API key) - Network accessline 236
- BioGRID: https://thebiogrid.org/ (register for free API key) - Network accessline 237
- SASBDB: https://www.sasbdb.org/ - Network accessline 237
- SASBDB: https://www.sasbdb.org/ - Network accessline 237
- SASBDB: https://www.sasbdb.org/ - Network accessline 238
- Complex Portal: https://www.ebi.ac.uk/complexportal/ - Network accessline 238
- Complex Portal: https://www.ebi.ac.uk/complexportal/ - Network accessline 238
- Complex Portal: https://www.ebi.ac.uk/complexportal/ - Network accessline 239
- ToolUniverse: https://github.com/mims-harvard/ToolUniverse - Network accessline 239
- ToolUniverse: https://github.com/mims-harvard/ToolUniverse - Network accessline 239
- ToolUniverse: https://github.com/mims-harvard/ToolUniverse
plugin/skills/tooluniverse-protein-interactions/TOOL_DESCRIPTION_OPTIMIZATION.md
- Network accessline 33
"Query protein and genetic interactions from the BioGRID database. **Prerequisites**: Requires BIOGRID_API_KEY environment variable (request at https://webservice.thebiogrid.org/). Returns experimentally - Network accessline 34
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Network accessline 35
(request at https://webservice.thebiogrid.org/). Returns experimentally - Network accessline 94
- `species`: "NCBI taxonomy ID specifying organism. Common values: 9606 (Homo sapiens/human), 10090 (Mus musculus/mouse), 10116 (Rattus norvegicus/rat), 7227 (Drosophila melanogaster). Default 9606 (human). Find taxonomy IDs at https://www. - Network accessline 228
for Interaction Datasets). Returns curated interactions from published studies with evidence methods, PubMed citations, and throughput information. **Prerequisites**: Requires BIOGRID_API_KEY environment variable (free academic API key at h - Access to secretsline 229
[Credential-related source omitted] - Network accessline 230
variable (free academic API key at https://webservice.thebiogrid.org/). - Network accessline 267
ubiquitination, acetylation, methylation, and other covalent protein modifications with site positions, modifying enzymes, and literature evidence. **Prerequisites**: Requires BIOGRID_API_KEY environment variable (request at https://webserv - Access to secretsline 268
[Credential-related source omitted] - Network accessline 269
variable (request at https://webservice.thebiogrid.org/). - Access to secretsline 296
[Credential-related source omitted] - Access to secretsline 326
[Credential-related source omitted] - Network accessline 336
**Parameter Improvements**: - `pubmed_ids`: "List of PubMed IDs (PMIDs) to query. **Note: plural parameter** - pass as list. Example: ['17200106'] (single paper) or ['17200106', '12345678'] (multiple papers). Returns all protein interaction - Network accessline 337
- `pubmed_ids`: "List of PubMed IDs (PMIDs) to query. **Note: plural parameter** - pass as list. Example: ['17200106'] (single paper) or ['17200106', '12345678'] (multiple papers). Returns all protein interactions curated from these publica - Network accessline 389
**Parameter Improvements**: - `sasbdb_id`: "SASBDB entry identifier. Format: 'SASDXXX' where XXX is alphanumeric (e.g., 'SASDAB7', 'SASD1P8'). Find IDs using SASBDB_search_entries or at https://www.sasbdb.org/. Example: 'SASDAB7' (p53 core - Network accessline 390
- `sasbdb_id`: "SASBDB entry identifier. Format: 'SASDXXX' where XXX is alphanumeric (e.g., 'SASDAB7', 'SASD1P8'). Find IDs using SASBDB_search_entries or at https://www.sasbdb.org/. Example: 'SASDAB7' (p53 core domain structure)." - Access to secretsline 439
[Credential-related source omitted]
plugin/skills/tooluniverse-protein-interactions/python_implementation.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-protein-lof-mechanism/SKILL.md
- Network accessline 55
[Credential-related source omitted] - Access to secretsline 55
[Credential-related source omitted] - Network accessline 56
- **esm package with SAE support**: `pip install 'esm @ git+https://github.com/evolutionaryscale/esm@ee891c52'` - Network accessline 256
git clone https://github.com/Kuhlman-Lab/ThermoMPNN.git - Network accessline 276
- [**Tamarind Bio**](https://www.tamarind.bio/tools/thermompnn) — REST API at `app.tamarind.bio/api/`, `x-api-key` auth - Network accessline 277
- [**Neurosnap**](https://neurosnap.ai/service/ThermoMPNN) — credit-based, HTTP API - Network accessline 278
- [**BioLM**](https://biolm.ai/models/thermompnn/) — `POST /api/v3/thermompnn/predict/`, Token auth - Network accessline 279
- [**ProteinIQ**](https://proteiniq.io/app/thermompnn) — web-first - Network accessline 280
- [**Levitate Bio**](https://support.levitate.bio/api/api-thermompnn/) — REST API
plugin/skills/tooluniverse-protein-modification-analysis/SKILL.md
- Network accessline 98
**Known broken (verified live, not a guess):** `EBIProteins_get_rna_editing` returns HTTP 404 directly from `https://www.ebi.ac.uk/proteins/api/rna_editing/...` for both of its own documented example accessions (P42262, P28335), while the s
plugin/skills/tooluniverse-protein-sae-variant-interpretation/SKILL.md
- Network accessline 44
[Credential-related source omitted] - Access to secretsline 44
[Credential-related source omitted] - Network accessline 47
pip install 'esm @ git+https://github.com/evolutionaryscale/esm@ee891c52' - Network accessline 51
**License note**: SAE outputs from Forge are governed by the [Cambrian Inference Clickthrough License](https://www.evolutionaryscale.ai/policies/cambrian-inference-clickthrough-license-agreement) — non-commercial / academic research use onl
plugin/skills/tooluniverse-protein-structure-prediction/SKILL.md
plugin/skills/tooluniverse-protein-structure-retrieval/examples.md
- Network accessline 59
# download_text_content (returns {"content", "size", "url", ...}). pdb_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 60
pdb_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" ) - Network accessline 61
url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 66
# Also get as mmCIF (modern format) cif_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 67
cif_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.cif" ) - Network accessline 68
url=f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 211
# Get all available formats from RCSB via download_text_content base = f"https://files.rcsb.org/download/{pdb_id}" - Network accessline 212
base = f"https://files.rcsb.org/download/{pdb_id}" - Network accessline 242
for pdb_id in candidates[:3]: structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id.upper()}.pdb" - Network accessline 243
structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id.upper()}.pdb" ) - Network accessline 244
url=f"https://files.rcsb.org/download/{pdb_id.upper()}.pdb"
plugin/skills/tooluniverse-protein-therapeutic-design/CHECKLIST.md
plugin/skills/tooluniverse-protein-therapeutic-design/DESIGN_PROCEDURES.md
- Access to secretsline 50
[Credential-related source omitted]
plugin/skills/tooluniverse-protein-therapeutic-design/SKILL.md
- Access to secretsline 86
[Credential-related source omitted] - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 89
[Credential-related source omitted] - Access to secretsline 90
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 97
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 102
[Credential-related source omitted]
plugin/skills/tooluniverse-protein-therapeutic-design/TOOLS_REFERENCE.md
- Access to secretsline 12
[Credential-related source omitted] - Network accessline 158
|------|---------|----------------| | `PDBeSIFTS_get_best_structures` | Find PDB structures for a UniProt accession (best first) | `uniprot_accession` | | `download_text_content` | Download a PDB file (no dedicated PDB-download tool exists) - Network accessline 159
| `PDBeSIFTS_get_best_structures` | Find PDB structures for a UniProt accession (best first) | `uniprot_accession` | | `download_text_content` | Download a PDB file (no dedicated PDB-download tool exists) | `url` (`https://files.rcsb.org/do - Network accessline 160
| `download_text_content` | Download a PDB file (no dedicated PDB-download tool exists) | `url` (`https://files.rcsb.org/download/<PDB_ID>.pdb`) | - Network accessline 169
pdb_id = structures[0]["pdb_id"].upper() structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 170
structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" )["content"] - Network accessline 171
url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 205
# Get the atomic model (PDB) for design structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 206
structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" )["content"] - Network accessline 207
url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Access to secretsline 422
[Credential-related source omitted] - Access to secretsline 430
[Credential-related source omitted] - Access to secretsline 432
[Credential-related source omitted]
plugin/skills/tooluniverse-proteomics-analysis/SKILL.md
- Network accessline 166
**Databases**: [STRING](https://string-db.org), [PhosphoSitePlus](https://www.phosphosite.org), [CORUM](https://mips.helmholtz-muenchen.de/corum)
plugin/skills/tooluniverse-proteomics-data-retrieval/SKILL.md
plugin/skills/tooluniverse-publish-tool/SKILL.md
- Network accessline 16
- If you are connected to the hosted ToolUniverse (you have `list_remote_tools`), call it and look for the user's computer ("your computer ...") and the tool. - Otherwise ask the user whether https://connect.aiscientist.tools/remote-servers - Network accessline 17
- Otherwise ask the user whether https://connect.aiscientist.tools/remote-servers shows the computer **online** with the tool listed. - Network accessline 43
1. Open https://connect.aiscientist.tools/my-tools?create=1 (sign in if asked). The **Publish a tool** window opens. - Network accessline 54
- **Public**: the tool should appear in https://connect.aiscientist.tools/discover (Tools view, "Shared by scientists"). If you have `list_remote_tools`, search for it by name and run it once with the same example.
plugin/skills/tooluniverse-rare-disease-diagnosis/CHECKLIST.md
- Access to secretsline 51
[Credential-related source omitted]
plugin/skills/tooluniverse-rare-disease-diagnosis/DIAGNOSTIC_WORKFLOW.md
- Access to secretsline 493
[Credential-related source omitted]
plugin/skills/tooluniverse-rare-disease-diagnosis/TOOLS_REFERENCE.md
- Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Network accessline 323
[Credential-related source omitted] - Access to secretsline 323
[Credential-related source omitted] - Access to secretsline 804
[Credential-related source omitted] - Access to secretsline 809
[Credential-related source omitted]
plugin/skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-regulatory-genomics/SKILL.md
plugin/skills/tooluniverse-regulatory-variant-analysis/SKILL.md
plugin/skills/tooluniverse-rnaseq-deseq2/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugin/skills/tooluniverse-rnaseq-deseq2/scripts/convert_rds_to_csv.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 177
print(" macOS: brew install r") print(" Ubuntu: sudo apt-get install r-base") print(" Or visit: https://cran.r-project.org/") - Dangerous shell operationline 178
print(" Ubuntu: sudo apt-get install r-base") - Network accessline 178
print(" Ubuntu: sudo apt-get install r-base") print(" Or visit: https://cran.r-project.org/") sys.exit(1) - Network accessline 179
print(" Or visit: https://cran.r-project.org/")
plugin/skills/tooluniverse-rnaseq-deseq2/scripts/format_deseq2_output.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-rnaseq-deseq2/scripts/gene_length_correlation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-rnaseq-deseq2/scripts/load_count_matrix.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-rnaseq-deseq2/scripts/multi_strain_venn.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-rnaseq-deseq2/scripts/one_way_anova_f.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-rnaseq-deseq2/scripts/pca_variance.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-rnaseq-deseq2/scripts/r_deseq2_wrapper.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-rnaseq-deseq2/scripts/r_edger_limma_wrapper.py
plugin/skills/tooluniverse-scvi-tools/scripts/_scvi_common.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-scvi-tools/scripts/differential_expression.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-scvi-tools/scripts/prepare_data.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-scvi-tools/scripts/train_model.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-scvi-tools/scripts/transfer_labels.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-scvi-tools/scripts/validate_adata.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-sdk/REFERENCE.md
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[Credential-related source omitted] - Network accessline 254
client = ToolUniverseClient("http://server:8080") - Access to secretsline 386
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[Credential-related source omitted] - Network accessline 497
- **Main Documentation**: https://zitniklab.hms.harvard.edu/ToolUniverse/ - Network accessline 498
- **API Reference**: https://zitniklab.hms.harvard.edu/ToolUniverse/api/modules.html - Network accessline 499
- **Tutorials**: https://zitniklab.hms.harvard.edu/ToolUniverse/guide/scientific_workflows.html - Network accessline 500
- **GitHub Examples**: https://github.com/mims-harvard/ToolUniverse/tree/main/examples - Network accessline 501
- **Community Slack**: https://join.slack.com/t/tooluniversehq/shared_invite/zt-3dic3eoio-5xxoJch7TLNibNQn5_AREQ
plugin/skills/tooluniverse-sdk/SKILL.md
plugin/skills/tooluniverse-self-review/references/ret-scored-evaluation.md
plugin/skills/tooluniverse-sequence-analysis/scripts/amino_acids.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-sequence-analysis/scripts/biology_facts.py
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Executable file
plugin/skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 260
url = f"https://rest.uniprot.org/uniprotkb/{accession}.fasta" try: with urllib.request.urlopen(url, timeout=15) as resp: - Network accessline 261
try: with urllib.request.urlopen(url, timeout=15) as resp: fasta = resp.read().decode("utf-8") - Network accessline 262
with urllib.request.urlopen(url, timeout=15) as resp:
plugin/skills/tooluniverse-sequence-analysis/scripts/translate_dna.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-share-computer/SKILL.md
- Dangerous shell operationline 36
curl -LsSf https://connect.aiscientist.tools/install.sh | sh - Network accessline 36
curl -LsSf https://connect.aiscientist.tools/install.sh | sh - Network accessline 42
powershell -ExecutionPolicy ByPass -c "irm https://connect.aiscientist.tools/install.ps1 | iex" - Network accessline 50
python -m pip install -r https://connect.aiscientist.tools/install-requirements.txt - Network accessline 88
If they already run an MCP server (Streamable HTTP, for example `http://localhost:8080/mcp`), share it unchanged with `--forward` in step 4. Confirm it answers MCP `tools/list` first; a web page or REST API is not MCP, so wrap it as in path - Network accessline 117
Authorize this computer in your browser: https://connect.aiscientist.tools/activate?user_code=ABCD-EFGH - Network accessline 118
https://connect.aiscientist.tools/activate?user_code=ABCD-EFGH - Network accessline 133
# A. own code ~/.local/bin/tu serve share_tool.py --share --detach --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 134
~/.local/bin/tu serve share_tool.py --share --detach --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 135
# B. existing MCP program ~/.local/bin/tu serve --forward "http://localhost:8080" --share --detach --name "Smith Lab Server" --service https://tooluniverse-backend.onrender.com # C. reviewed models - Network accessline 136
~/.local/bin/tu serve --forward "http://localhost:8080" --share --detach --name "Smith Lab Server" --service https://tooluniverse-backend.onrender.com - Network accessline 138
~/.local/bin/tu serve --allow esm,boltz --share --detach --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 149
- If you are connected to the hosted ToolUniverse (you have `list_remote_tools`), call `list_remote_tools` with the computer's name, then `run_remote_tool` once with the same example you tested in step 3. Compare the answer with the local r - Network accessline 150
- Otherwise ask the user to open https://connect.aiscientist.tools/remote-servers and confirm the computer shows as online with the expected tools. - Network accessline 157
~/.local/bin/tuplatform-service install --tool-file "$PWD/share_tool.py" --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 166
- **Specific colleagues use this computer's tools**: on https://connect.aiscientist.tools/remote-servers, the computer's card has **Create share code**. The user copies the code (or **Copy invitation**) and sends it. A colleague pastes it o
plugin/skills/tooluniverse-single-cell/.env.template
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plugin/skills/tooluniverse-single-cell/scripts/find_markers.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-single-cell/scripts/normalize_data.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-single-cell/scripts/qc_metrics.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-single-cell/scripts/scrna_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-spatial-transcriptomics/SKILL.md
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geo_id = "GSE123456" # Check for h5ad or MTX in supplementary files url = f"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={geo_id}&targ=gsm&view=data" - Network accessline 318
url = f"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={geo_id}&targ=gsm&view=data" - Network accessline 323
# HuBMAP data portal # Search at https://portal.hubmapconsortium.org/search then download via globus or direct link - Network accessline 324
# Search at https://portal.hubmapconsortium.org/search then download via globus or direct link - Network accessline 325
# Human Cell Atlas: https://data.humancellatlas.org/ — download h5ad/loom files - Network accessline 345
**Methods**: - Squidpy: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 346
- Squidpy: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 347
- Cell2location: https://doi.org/10.1038/s41587-021-01139-4 - Network accessline 348
- SpatialDE: https://doi.org/10.1038/nmeth.4636 - Network accessline 350
**Platforms**: - 10x Visium: https://www.10xgenomics.com/products/spatial-gene-expression - Network accessline 351
- 10x Visium: https://www.10xgenomics.com/products/spatial-gene-expression - Network accessline 352
- MERFISH: https://doi.org/10.1126/science.aaa6090 - Network accessline 353
- Slide-seq: https://doi.org/10.1126/science.aaw1219
plugin/skills/tooluniverse-statistical-modeling/SKILL.md
plugin/skills/tooluniverse-statistical-modeling/scripts/expression_anova.py
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#!/usr/bin/env python3
plugin/skills/tooluniverse-statistical-modeling/scripts/format_statistical_output.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-statistical-modeling/scripts/logistic_regression_or.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-statistical-modeling/scripts/model_diagnostics.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-statistical-modeling/scripts/power_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-statistical-modeling/scripts/prepare_ae_cohort.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-statistical-modeling/scripts/r_natural_spline_regression.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-statistical-modeling/scripts/sdtm_ordinal_logistic.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-statistical-modeling/scripts/spline_model_compare.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-statistical-modeling/scripts/stat_tests.py
- Executable scriptline 1
Executable file
plugin/skills/tooluniverse-structural-proteomics/SKILL.md
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- PDBePISA: `operation` is internal, not a public parameter - BMRB: `BMRB_get_entries_by_pdb_id` is a BLAST-based sequence match, not a curated 1:1 PDB<->BMRB cross-reference -- a query can return several candidate entries (or none) for a r - Network accessline 312
- BMRB: `BMRB_get_entries_by_pdb_id` is a BLAST-based sequence match, not a curated 1:1 PDB<->BMRB cross-reference -- a query can return several candidate entries (or none) for a real NMR PDB ID; `BMRB_search_chemical_shifts` is slow (15-20 - Network accessline 313
- MobiDB: `MobiDB_get_protein` and `MobiDB_get_consensus` were verified live (repeatedly, with a 90s timeout) to hang indefinitely against their configured endpoint (`https://mobidb.org/api/download`) -- confirmed independently via direct `
plugin/skills/tooluniverse-structural-variant-analysis/ANALYSIS_PROCEDURES.md
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# Note: ToolUniverse has no DECIPHER tool - look up similar patient cases # manually in the DECIPHER web portal (https://www.deciphergenomics.org). - Network accessline 131
# manually in the DECIPHER web portal (https://www.deciphergenomics.org). - Network accessline 216
# Note: no DECIPHER tool exists in ToolUniverse - check DECIPHER cohorts # for these genes manually at https://www.deciphergenomics.org. - Network accessline 217
# for these genes manually at https://www.deciphergenomics.org.
plugin/skills/tooluniverse-structural-variant-analysis/SKILL.md
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- ClinGen Dosage Sensitivity Map: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/
plugin/skills/tooluniverse-synthetic-biology-parts/SKILL.md
- Network accessline 31
$ tu run SynBioHub_get_part '{"display_id": "BBa_E0040"}' Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely - Network accessline 32
Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely an HTML login or error page). The part may not exist, or SynBioHub may now - Network accessline 33
'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely - Network accessline 42
1. Tell the user plainly: SynBioHub now requires authentication for these reads; this is an upstream policy change, not a bug in the request. 2. Point them to create a free account at https://synbiohub.org, log in, - Network accessline 43
reads; this is an upstream policy change, not a bug in the request. 2. Point them to create a free account at https://synbiohub.org, log in, and retrieve a session token, then set `SYNBIOHUB_API_TOKEN` in the - Network accessline 44
2. Point them to create a free account at https://synbiohub.org, log in, - Access to secretsline 71
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deliberately hides any tool with unmet `required_api_keys` until the key is present — verified, not a bug); register for a free key at https://developers.addgene.org/ if it's missing. - Network accessline 73
present — verified, not a bug); register for a free key at https://developers.addgene.org/ if it's missing. - Cloning workflow steps (restriction sites, Gibson/Golden Gate assembly - Network accessline 74
https://developers.addgene.org/ if it's missing.
plugin/skills/tooluniverse-synthetic-biology-parts/references/synbiohub_tool_reference.md
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$ tu run SynBioHub_get_part '{"display_id": "BBa_E0040"}' Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, - Network accessline 33
Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely an HTML login or error page). The part may not exist, or - Network accessline 34
'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, - Network accessline 38
$ tu run SynBioHub_get_part '{"part_uri": "https://synbiohub.org/public/igem/BBa_E0040/1"}' - Network accessline 39
Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely an HTML login or error page). The part may not exist, or - Network accessline 40
'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, - Network accessline 84
- **Parameters:** exactly one of `display_id` (string, e.g. `BBa_E0040` for iGEM BioBricks) or `part_uri` (string, full SynBioHub URI, e.g. `https://synbiohub.org/public/igem/BBa_E0040/1` — typically taken from - Network accessline 85
for iGEM BioBricks) or `part_uri` (string, full SynBioHub URI, e.g. `https://synbiohub.org/public/igem/BBa_E0040/1` — typically taken from a search result's `uri` field). Both are individually optional/nullable - Network accessline 86
`https://synbiohub.org/public/igem/BBa_E0040/1` — typically taken from - Access to secretsline 105
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https://developers.addgene.org/) to use Addgene lookups alongside this
plugin/skills/tooluniverse-systems-biology/.env.template
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plugin/skills/tooluniverse-systems-biology/python_implementation.py
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#!/usr/bin/env python3
plugin/skills/tooluniverse-target-research/IMPLEMENTATION.md
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plugin/skills/tooluniverse-target-research/REFERENCE.md
plugin/skills/tooluniverse-target-research/SKILL.md
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plugin/skills/tooluniverse-toxicology/SKILL.md
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- AOPWiki: https://aopwiki.org - Network accessline 371
- FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers - Network accessline 372
- CTD: http://ctdbase.org - Network accessline 373
- DailyMed: https://dailymed.nlm.nih.gov - Network accessline 374
- OpenFDA: https://open.fda.gov - Network accessline 375
- EPA CompTox: https://comptox.epa.gov/dashboard/ (API docs: https://comptox.epa.gov/ctx-api/docs/)
plugin/skills/tooluniverse-use-shared-computer/SKILL.md
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1. Open https://connect.aiscientist.tools/dashboard (sign in if asked). - Network accessline 25
- Call `list_remote_tools` (optionally with words from the task, such as "protein" or the computer's name). It lists tools on the user's own computers, computers they joined, and published tools, each with an id, a description, its inputs a - Network accessline 26
- Published tools can also be browsed at https://connect.aiscientist.tools/discover ("Shared by scientists"). - Network accessline 41
The user can leave a shared computer on https://connect.aiscientist.tools/remote-servers: its card has **Stop using**. The owner can also turn the code off at any time.
plugin/skills/tooluniverse-vaccine-design/scripts/population_coverage.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-variant-analysis/.env.template
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plugin/skills/tooluniverse-variant-analysis/python_implementation.py
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#!/usr/bin/env python3
plugin/skills/tooluniverse-variant-analysis/scripts/annotate_variants.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-variant-analysis/scripts/coding_variant_filter.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-variant-analysis/scripts/filter_variants.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-variant-analysis/scripts/gatk_haplotypecaller_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-variant-analysis/scripts/parse_vcf.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-variant-analysis/scripts/variant_fraction.py
- Executable scriptline 1
#!/usr/bin/env python3
plugin/skills/tooluniverse-variant-interpretation/CHECKLIST.md
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plugin/skills/tooluniverse-variant-interpretation/SKILL.md
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plugin/skills/tooluniverse-variant-interpretation/TOOLS_REFERENCE.md
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plugin/skills/tooluniverse-variant-predictor-dms-validation/SKILL.md
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# The official DeepMind release contains per-variant continuous scores # (not just bin assignments). Not currently wrapped by a TU tool — fetch directly: # https://alphafold.ebi.ac.uk/files/AF-<uniprot_id>-F1-aa-substitutions.csv - Network accessline 183
# (not just bin assignments). Not currently wrapped by a TU tool — fetch directly: # https://alphafold.ebi.ac.uk/files/AF-<uniprot_id>-F1-aa-substitutions.csv # or stream-filter https://storage.googleapis.com/dm_alphamissense/AlphaMissens - Network accessline 184
# https://alphafold.ebi.ac.uk/files/AF-<uniprot_id>-F1-aa-substitutions.csv - Network accessline 185
# or stream-filter https://storage.googleapis.com/dm_alphamissense/AlphaMissense_aa_substitutions.tsv.gz - Access to secretsline 206
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plugin/skills/tooluniverse-variant-to-mechanism/SKILL.md
- Access to secretsline 258
[Credential-related source omitted] - Network accessline 350
# Ensembl VEP REST — functional consequence prediction vep = requests.get(f"https://rest.ensembl.org/vep/human/id/{rsid}?content-type=application/json").json() - Network accessline 351
vep = requests.get(f"https://rest.ensembl.org/vep/human/id/{rsid}?content-type=application/json").json() - Network accessline 353
# GTEx eQTL — tissue-specific expression effects eqtl = requests.get(f"https://gtexportal.org/api/v2/association/singleTissueEqtl", - Network accessline 354
eqtl = requests.get(f"https://gtexportal.org/api/v2/association/singleTissueEqtl", - Network accessline 357
# ClinVar bulk (variant_summary.txt.gz, ~200MB, all variants) # Download once, filter locally: # df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") - Network accessline 358
# Download once, filter locally: # df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") # hits = df[df["RS# (dbSNP)"] == int(rsid.replace("rs",""))] - Network accessline 359
# df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t")
plugin/skills/tooluniverse/.env.template
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plugin/skills/tooluniverse/REFERENCE.md
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plugins/tooluniverse/skills/setup-tooluniverse/API_KEYS_REFERENCE.md
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- **What it does**: Increases PubMed API rate limit from 3 requests/second to 10 requests/second. Without it, heavy literature searches may be throttled. - **How to get it**: 1. Go to https://account.ncbi.nlm.nih.gov/ and sign in (via Goo - Network accessline 15
- **How to get it**: 1. Go to https://account.ncbi.nlm.nih.gov/ and sign in (via Google, ORCiD, eRA Commons, or Login.gov) 2. Go to Settings at https://account.ncbi.nlm.nih.gov/settings/ - Network accessline 16
1. Go to https://account.ncbi.nlm.nih.gov/ and sign in (via Google, ORCiD, eRA Commons, or Login.gov) - Network accessline 17
2. Go to Settings at https://account.ncbi.nlm.nih.gov/settings/ - Access to secretsline 21
[Credential-related source omitted] - Access to secretsline 23
[Credential-related source omitted] - Network accessline 28
- **What it does**: Provides access to GPU-accelerated bioinformatics models hosted by NVIDIA. Covers protein structure prediction, molecular docking, and sequence analysis. - **How to get it**: 1. Go to https://build.nvidia.com - Network accessline 29
- **How to get it**: 1. Go to https://build.nvidia.com 2. Sign in or create a free NVIDIA account - Network accessline 30
1. Go to https://build.nvidia.com - Access to secretsline 35
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- **What it does**: Enables querying the BioGRID database for protein-protein interactions, genetic interactions, and chemical associations. - **How to get it**: 1. Go to https://webservice.thebiogrid.org/ - Network accessline 44
- **How to get it**: 1. Go to https://webservice.thebiogrid.org/ 2. Click "Register" to create a free account - Network accessline 45
1. Go to https://webservice.thebiogrid.org/ - Access to secretsline 49
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- **What it does**: Provides access to one of the largest collections of gene-disease associations, integrating data from expert-curated repositories, GWAS catalogs, and animal models. - **How to get it**: 1. Go to https://disgenet.com/ac - Network accessline 57
- **How to get it**: 1. Go to https://disgenet.com/academic-apply (for academic/non-profit use) 2. Fill out the application with your institutional email - Network accessline 58
1. Go to https://disgenet.com/academic-apply (for academic/non-profit use) - Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 70
[Credential-related source omitted] - Network accessline 75
- **What it does**: Provides access to the authoritative database of human genes and genetic phenotypes, particularly for inherited diseases. - **How to get it**: 1. Go to https://omim.org/api - Network accessline 76
- **How to get it**: 1. Go to https://omim.org/api 2. Click "Request API Key" - Network accessline 77
1. Go to https://omim.org/api - Access to secretsline 81
[Credential-related source omitted] - Network accessline 89
- **What it does**: Provides access to MSK's precision oncology knowledge base with FDA-recognized biomarker-drug associations, levels of evidence for therapeutic implications. - **How to get it**: 1. Go to https://www.oncokb.org/apiAcces - Network accessline 90
- **How to get it**: 1. Go to https://www.oncokb.org/apiAccess 2. Click "Request API Access" - Network accessline 91
1. Go to https://www.oncokb.org/apiAccess - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Network accessline 104
- **What it does**: Maps between medical terminologies, finds concept definitions, and navigates hierarchical relationships between medical concepts. - **How to get it**: 1. Go to https://uts.nlm.nih.gov/uts/ - Network accessline 105
- **How to get it**: 1. Go to https://uts.nlm.nih.gov/uts/ 2. Click "Sign Up" to create a UMLS Terminology Services account - Network accessline 106
1. Go to https://uts.nlm.nih.gov/uts/ - Access to secretsline 110
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Network accessline 118
- **What it does**: Enables searching and retrieving US patent documents, patent claims, and patent family information. - **How to get it**: 1. Create a USPTO account at https://my.uspto.gov/ (if you don't have one) - Network accessline 119
- **How to get it**: 1. Create a USPTO account at https://my.uspto.gov/ (if you don't have one) 2. Go to the API manager at https://account.uspto.gov/api-manager/ - Network accessline 120
1. Create a USPTO account at https://my.uspto.gov/ (if you don't have one) - Network accessline 121
2. Go to the API manager at https://account.uspto.gov/api-manager/ - Access to secretsline 124
[Credential-related source omitted] - Access to secretsline 126
[Credential-related source omitted] - Network accessline 131
- **What it does**: Provides an isolated authenticated quota (new keys currently start at 1 request/second) instead of relying on the dynamically throttled anonymous pool. It also enables authentication-required endpoints. - **How to get it - Network accessline 132
- **How to get it**: 1. Go to https://www.semanticscholar.org/product/api 2. Click "Request API Key" - Network accessline 133
1. Go to https://www.semanticscholar.org/product/api - Access to secretsline 137
[Credential-related source omitted] - Access to secretsline 139
[Credential-related source omitted] - Network accessline 144
- **What it does**: Increases openFDA rate limit from 240 requests/minute to 1000 requests/minute. Useful for drug safety signal analysis. - **How to get it**: 1. Go to https://open.fda.gov/apis/authentication/ - Network accessline 145
- **How to get it**: 1. Go to https://open.fda.gov/apis/authentication/ 2. Enter your email to request a key - Network accessline 146
1. Go to https://open.fda.gov/apis/authentication/ - Access to secretsline 149
[Credential-related source omitted] - Network accessline 156
- **What it does**: Provides access to the world's most comprehensive enzyme database with kinetic parameters (Km, Vmax, kcat), substrate/product data, and enzyme classifications. - **How to get it**: 1. Go to https://brenda-enzymes.org/r - Network accessline 157
- **How to get it**: 1. Go to https://brenda-enzymes.org/register.php 2. Fill in email, password, name, and scientific field - Network accessline 158
1. Go to https://brenda-enzymes.org/register.php - Access to secretsline 166
[Credential-related source omitted] - Network accessline 171
- **What it does**: Provides access to Mouser's comprehensive catalog of electronic components, including ICs, resistors, capacitors, microcontrollers, and more. Includes pricing, availability, datasheets, and specifications. - **How to get - Network accessline 172
- **How to get it**: 1. Go to https://www.mouser.com/api-search/ 2. Click "Sign Up For Search API" - Network accessline 173
1. Go to https://www.mouser.com/api-search/ - Access to secretsline 177
[Credential-related source omitted] - Access to secretsline 180
[Credential-related source omitted] - Network accessline 185
- **What it does**: Provides access to Digi-Key's extensive catalog of electronic components with detailed specifications, pricing, inventory, parametric search, and technical documentation. - **How to get it**: 1. Go to https://developer - Network accessline 186
- **How to get it**: 1. Go to https://developer.digikey.com/ 2. Click "Register" to create a free developer account - Network accessline 187
1. Go to https://developer.digikey.com/ - Network accessline 189
3. After login, go to "Organization" → "Production Apps" 4. Click "Create Production App" 5. Fill in app details (name, description, OAuth callback URL: `https://localhost/callback`) - Network accessline 190
4. Click "Create Production App" 5. Fill in app details (name, description, OAuth callback URL: `https://localhost/callback`) 6. After creation, copy the **Client ID** and **Client Secret** - Network accessline 191
5. Fill in app details (name, description, OAuth callback URL: `https://localhost/callback`) - Access to secretsline 193
[Credential-related source omitted] - Access to secretsline 205
[Credential-related source omitted] - Network accessline 209
- **What it does**: Powers agentic tools using Google's Gemini models. Good default choice due to generous free tier. - **How to get it**: 1. Go to https://aistudio.google.com/apikey - Network accessline 210
- **How to get it**: 1. Go to https://aistudio.google.com/apikey 2. Sign in with your Google account - Network accessline 211
1. Go to https://aistudio.google.com/apikey - Access to secretsline 215
[Credential-related source omitted] - Access to secretsline 218
[Credential-related source omitted] - Network accessline 222
- **What it does**: Provides access to 100+ LLM models through a single API. Pay-per-use pricing, good flexibility. - **How to get it**: 1. Go to https://openrouter.ai/ - Network accessline 223
- **How to get it**: 1. Go to https://openrouter.ai/ 2. Sign up or log in - Network accessline 224
1. Go to https://openrouter.ai/ - Network accessline 225
2. Sign up or log in 3. Go to https://openrouter.ai/keys 4. Click "Create Key" - Network accessline 226
3. Go to https://openrouter.ai/keys - Access to secretsline 229
[Credential-related source omitted] - Access to secretsline 231
[Credential-related source omitted] - Network accessline 235
- **What it does**: Powers embedding-based tool finding and can serve as an LLM provider for agentic features. - **How to get it**: 1. Go to https://platform.openai.com/ - Network accessline 236
- **How to get it**: 1. Go to https://platform.openai.com/ 2. Sign up or log in - Network accessline 237
1. Go to https://platform.openai.com/ - Access to secretsline 242
[Credential-related source omitted] - Access to secretsline 244
[Credential-related source omitted] - Network accessline 248
- **What it does**: Same capabilities as OpenAI but through Azure infrastructure. Preferred for enterprise/institutional users. - **How to get it**: 1. Go to Azure Portal (https://portal.azure.com) - Network accessline 249
- **How to get it**: 1. Go to Azure Portal (https://portal.azure.com) 2. Create or navigate to your Azure OpenAI resource - Network accessline 250
1. Go to Azure Portal (https://portal.azure.com) - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 257
[Credential-related source omitted] - Network accessline 261
- **What it does**: Enables Claude-based features. - **How to get it**: 1. Go to https://console.anthropic.com/ - Network accessline 262
- **How to get it**: 1. Go to https://console.anthropic.com/ 2. Sign up or log in - Network accessline 263
1. Go to https://console.anthropic.com/ - Access to secretsline 267
[Credential-related source omitted] - Network accessline 273
- **What it does**: Enables access to gated HuggingFace models and datasets, and the HF Inference API for embeddings. - **How to get it**: 1. Go to https://huggingface.co/settings/tokens - Network accessline 274
- **How to get it**: 1. Go to https://huggingface.co/settings/tokens 2. Sign up or log in - Network accessline 275
1. Go to https://huggingface.co/settings/tokens - Access to secretsline 280
[Credential-related source omitted] - Access to secretsline 289
[Credential-related source omitted] - Access to secretsline 290
[Credential-related source omitted] - Access to secretsline 291
[Credential-related source omitted] - Access to secretsline 292
[Credential-related source omitted] - Access to secretsline 293
[Credential-related source omitted] - Access to secretsline 295
[Credential-related source omitted] - Access to secretsline 296
[Credential-related source omitted] - Access to secretsline 297
[Credential-related source omitted] - Access to secretsline 298
[Credential-related source omitted]
plugins/tooluniverse/skills/setup-tooluniverse/CLAUDE_DESKTOP.md
- Network accessline 5
Claude Desktop is a GUI app. It launches in a clean environment and **does not inherit your shell's PATH**. This means if `uvx` lives in `~/.local/bin/uvx` (the default for the uv curl installer), Claude Desktop cannot find it — and will sh - Network accessline 19
If you already installed uv via the curl installer, also run: - Network accessline 55
- Homebrew Apple Silicon: `/opt/homebrew/bin/uvx` - Homebrew Intel: `/usr/local/bin/uvx` - curl installer: `/Users/yourname/.local/bin/uvx` - Network accessline 57
- curl installer: `/Users/yourname/.local/bin/uvx` - Dangerous shell operationline 81
sudo ln -sf "$(which uvx)" /usr/local/bin/uvx - Network accessline 109
If the user installed uv via curl (not Homebrew), replace `'command': 'uvx'` with `'command': '/Users/yourname/.local/bin/uvx'` using their actual path from `which uvx`. - Network accessline 149
If `npx` is not found, install Node.js from [nodejs.org](https://nodejs.org) (LTS version includes npx), then retry. - Network accessline 153
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 157
rm -rf /tmp/tu-skills - Network accessline 178
✅ **Pass**: You see folders like `tooluniverse`, `tooluniverse-drug-research`, etc. → skills are ready. ❌ **Fail**: Nothing listed → wrong directory or install didn't complete. Check [SKILLS_CATALOG.md](https://raw.githubusercontent.com/mim - Network accessline 179
❌ **Fail**: Nothing listed → wrong directory or install didn't complete. Check [SKILLS_CATALOG.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/SKILLS_CATALOG.md) and retry.
plugins/tooluniverse/skills/setup-tooluniverse/EXAMPLES.md
- Network accessline 111
git clone https://github.com/mims-harvard/ToolUniverse.git - Dangerous shell operationline 232
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 232
curl -LsSf https://astral.sh/uv/install.sh | sh - Dangerous shell operationline 290
sudo apt install python3.12 python3.12-pip - Access to secretsline 373
[Credential-related source omitted] - Access to secretsline 374
[Credential-related source omitted] - Access to secretsline 375
[Credential-related source omitted] - Access to secretsline 388
[Credential-related source omitted] - Access to secretsline 389
[Credential-related source omitted]
plugins/tooluniverse/skills/setup-tooluniverse/GITHUB_ISSUE.md
- Network accessline 44
> > **Step 1.** Open this link in your browser: > https://github.com/mims-harvard/ToolUniverse/issues/new - Network accessline 45
> **Step 1.** Open this link in your browser: > https://github.com/mims-harvard/ToolUniverse/issues/new > - Network accessline 46
> https://github.com/mims-harvard/ToolUniverse/issues/new
plugins/tooluniverse/skills/setup-tooluniverse/INSTALL.md
- Network accessline 23
# Install uv first curl -LsSf https://astral.sh/uv/install.sh | sh - Dangerous shell operationline 24
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 24
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 42
git clone https://github.com/mims-harvard/ToolUniverse.git - Dangerous shell operationline 97
sudo apt update - Dangerous shell operationline 98
sudo apt install python3.12 python3.12-pip - Network accessline 104
Download from [python.org](https://www.python.org/downloads/) and select version 3.12. - Dangerous shell operationline 182
rm -rf ~/.cache/tooluniverse
plugins/tooluniverse/skills/setup-tooluniverse/MCP_CONFIG.md
- Network accessline 65
For full Trae setup instructions (including manual write fallback and verification), see [TRAE.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TRAE.md). - Access to secretsline 253
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 255
[Credential-related source omitted] - Access to secretsline 259
[Credential-related source omitted] - Access to secretsline 268
[Credential-related source omitted] - Access to secretsline 274
[Credential-related source omitted] - Network accessline 378
- Verify uvx works: `uvx --version` - Test ToolUniverse directly: `uvx tooluniverse --help` - If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - Network accessline 379
- Test ToolUniverse directly: `uvx tooluniverse --help` - If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - For pip-based installs: `pip show tooluniverse` and `which tooluniverse-smcp-stdio` - Dangerous shell operationline 380
- If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - Network accessline 380
- If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - Access to secretsline 436
[Credential-related source omitted]
plugins/tooluniverse/skills/setup-tooluniverse/SKILL.md
- Network accessline 43
**If Chat mode**, ask which app (AskQuestion): Cursor, Claude Desktop, VS Code/Copilot, Windsurf, Claude Code, Gemini CLI, Codex, Cline/Trae/Antigravity/OpenCode. "I don't have any" → Recommend [Claude Desktop](https://claude.ai/download). - Dangerous shell operationline 52
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 52
curl -LsSf https://astral.sh/uv/install.sh | sh - Access to secretsline 140
[Credential-related source omitted] - Network accessline 154
Read https://aiscientist.tools/setup.md and set up ToolUniverse for me. - Network accessline 241
| Key | Unlocks | Free? | Registration | |-----|---------|-------|-------------| | `NCBI_API_KEY` | PubMed (rate limit 3→10/s) | Yes | https://account.ncbi.nlm.nih.gov/settings/ | - Network accessline 242
|-----|---------|-------|-------------| | `NCBI_API_KEY` | PubMed (rate limit 3→10/s) | Yes | https://account.ncbi.nlm.nih.gov/settings/ | | `NVIDIA_API_KEY` | 16 tools: AlphaFold2, docking, genomics | Yes | https://build.nvidia.com | - Network accessline 243
[Credential-related source omitted] - Access to secretsline 243
[Credential-related source omitted] - Network accessline 244
[Credential-related source omitted] - Access to secretsline 244
[Credential-related source omitted] - Network accessline 245
[Credential-related source omitted] - Access to secretsline 245
[Credential-related source omitted] - Network accessline 246
[Credential-related source omitted] - Access to secretsline 246
[Credential-related source omitted] - Network accessline 250
| Key | Unlocks | Registration | |-----|---------|-------------| | `DISGENET_API_KEY` | Gene-disease associations | https://disgenet.com/academic-apply | - Network accessline 251
|-----|---------|-------------| | `DISGENET_API_KEY` | Gene-disease associations | https://disgenet.com/academic-apply | | `OMIM_API_KEY` | Mendelian/rare disease | https://omim.org/api | - Network accessline 252
[Credential-related source omitted] - Access to secretsline 252
[Credential-related source omitted] - Network accessline 253
[Credential-related source omitted] - Access to secretsline 253
[Credential-related source omitted] - Network accessline 254
| `ONCOKB_API_TOKEN` | Precision oncology | https://www.oncokb.org/apiAccess | - Network accessline 255
[Credential-related source omitted] - Access to secretsline 255
[Credential-related source omitted] - Access to secretsline 265
[Credential-related source omitted] - Access to secretsline 271
[Credential-related source omitted] - Access to secretsline 272
[Credential-related source omitted] - Network accessline 301
git clone --depth 1 https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 314
Clean up: `rm -rf /tmp/tu-skills` - Access to secretsline 394
[Credential-related source omitted] - Access to secretsline 395
[Credential-related source omitted] - Network accessline 432
Still stuck? [GitHub issues](https://github.com/mims-harvard/ToolUniverse/issues) or email [Shanghua Gao](mailto:shanghuagao@gmail.com). - Network accessline 437
- **Upgrade**: `uv cache clean tooluniverse` + restart - **All scientific API keys are free** - **Skills**: https://github.com/mims-harvard/ToolUniverse/tree/main/skills - Network accessline 439
- **Skills**: https://github.com/mims-harvard/ToolUniverse/tree/main/skills
plugins/tooluniverse/skills/setup-tooluniverse/TRAE.md
- Dangerous shell operationline 20
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 20
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 26
powershell -c "irm https://astral.sh/uv/install.ps1 | iex" - Network accessline 35
This should print help text. If it fails, see [TROUBLESHOOTING.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TROUBLESHOOTING.md). - Network accessline 129
If `npx` is not found, install Node.js from [nodejs.org](https://nodejs.org) (the LTS version includes npm and npx), then retry. - Network accessline 133
If `git` is not found, install it from [git-scm.com](https://git-scm.com), then retry. - Network accessline 137
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 140
rm -rf /tmp/tu-skills - Network accessline 145
# Run each line separately — PowerShell does not support && like bash git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git "$env:TEMP\tu-skills" - Network accessline 146
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git "$env:TEMP\tu-skills" - Network accessline 154
> Note: The exact skills directory for Trae may vary. Check [SKILLS_CATALOG.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/SKILLS_CATALOG.md) if skills don't activate after installation. - Network accessline 198
2. `uvx tooluniverse --help` works in your **system terminal** (not Trae's terminal) 3. Trae was fully restarted 4. Fetch [TROUBLESHOOTING.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TROUBL - Network accessline 200
4. Fetch [TROUBLESHOOTING.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TROUBLESHOOTING.md) for more diagnostics, or [GITHUB_ISSUE.md](https://raw.githubusercontent.com/mims-harvard/ToolUnive
plugins/tooluniverse/skills/setup-tooluniverse/TROUBLESHOOTING.md
- Dangerous shell operationline 12
# or: sudo apt install python3.12 # Ubuntu/Debian - Dangerous shell operationline 22
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 22
curl -LsSf https://astral.sh/uv/install.sh | sh - Dangerous shell operationline 64
sudo ln -sf "$(which uvx)" /usr/local/bin/uvx # Intel Mac / Linux - Dangerous shell operationline 66
sudo ln -sf "$(which uvx)" /opt/homebrew/bin/uvx - Access to secretsline 100
[Credential-related source omitted] - Access to secretsline 106
[Credential-related source omitted] - Access to secretsline 108
[Credential-related source omitted] - Network accessline 183
# In your MCP config args: "args": ["tooluniverse==<last-known-good-version>"] # Check releases: https://github.com/mims-harvard/ToolUniverse/releases - Network accessline 185
# Check releases: https://github.com/mims-harvard/ToolUniverse/releases - Network accessline 224
body = "\n".join(lines) title = "Bug: <brief description>" url = ("https://github.com/mims-harvard/ToolUniverse/issues/new" - Network accessline 225
title = "Bug: <brief description>" url = ("https://github.com/mims-harvard/ToolUniverse/issues/new" "?title=" + urllib.parse.quote(title) - Network accessline 226
url = ("https://github.com/mims-harvard/ToolUniverse/issues/new" "?title=" + urllib.parse.quote(title) + "&body=" + urllib.parse.quote(body)) - Network accessline 242
The script prints two things: 1. **Issue body** — copy-paste it into https://github.com/mims-harvard/ToolUniverse/issues/new - Network accessline 243
1. **Issue body** — copy-paste it into https://github.com/mims-harvard/ToolUniverse/issues/new
plugins/tooluniverse/skills/setup-tooluniverse/scripts/check_prerequisites.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 78
else: print(" ⚠<U+FE0F> NOT FOUND: uv not installed (recommended for MCP)") print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh") - Network accessline 79
print(" ⚠<U+FE0F> NOT FOUND: uv not installed (recommended for MCP)") print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh") return False - Dangerous shell operationline 80
print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh") - Network accessline 80
print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh")
plugins/tooluniverse/skills/setup-tooluniverse/scripts/diagnose_setup.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/setup-tooluniverse/scripts/list_tool_categories.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/setup-tooluniverse/scripts/verify_installation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-acmg-variant-classification/SKILL.md
- Access to secretsline 112
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-antibody-engineering/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-antibody-engineering/scripts/developability.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-antigravity-plugin/SKILL.md
- Dangerous shell operationline 14
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 14
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 15
agy --version # Antigravity CLI; if missing: https://antigravity.google - Network accessline 32
git clone https://github.com/mims-harvard/ToolUniverse.git - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 88
[Credential-related source omitted] - Access to secretsline 89
[Credential-related source omitted] - Network accessline 109
Still stuck: https://github.com/mims-harvard/ToolUniverse/issues
plugins/tooluniverse/skills/tooluniverse-binder-discovery/CHECKLIST.md
- Access to secretsline 23
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-binder-discovery/EXAMPLES.md
- Access to secretsline 385
[Credential-related source omitted] - Network accessline 456
# (no dedicated PDB-download tool; fetch the file from RCSB) pdb_content = tu.tools.download_text_content( url="https://files.rcsb.org/download/2W9Z.pdb" - Network accessline 457
pdb_content = tu.tools.download_text_content( url="https://files.rcsb.org/download/2W9Z.pdb" )['content'] - Network accessline 458
url="https://files.rcsb.org/download/2W9Z.pdb" - Access to secretsline 711
[Credential-related source omitted] - Access to secretsline 715
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-binder-discovery/SKILL.md
- Access to secretsline 92
[Credential-related source omitted] - Access to secretsline 93
[Credential-related source omitted] - Access to secretsline 129
[Credential-related source omitted] - Access to secretsline 142
[Credential-related source omitted] - Access to secretsline 167
[Credential-related source omitted] - Network accessline 258
# PubChem batch property retrieval (up to 100 CIDs per call) cids = "2244,5988,3672" url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" - Network accessline 259
cids = "2244,5988,3672" url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" props = pd.DataFrame(requests.get(url).json()["PropertyTable"]["Prope - Network accessline 260
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" - Network accessline 261
props = pd.DataFrame(requests.get(url).json()["PropertyTable"]["Properties"]) - Network accessline 263
# ChEMBL bioactivity bulk download for a target target_id = "CHEMBL203" # EGFR url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" - Network accessline 264
target_id = "CHEMBL203" # EGFR url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" activities = requests.get(url).json()["activities"] - Network accessline 265
url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" activities = requests.get(url).json()["activities"] df = pd.DataFrame(activities)[["molecule_chembl_id", "canonical_sm - Network accessline 266
activities = requests.get(url).json()["activities"] - Network accessline 273
# SDF download from PubChem (for docking input) sdf_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/SDF" - Network accessline 274
sdf_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/SDF" - Network accessline 275
sdf_content = requests.get(sdf_url).text - Access to secretsline 286
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-binder-discovery/TOOLS_REFERENCE.md
- Access to secretsline 214
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 472
[Credential-related source omitted] - Access to secretsline 580
[Credential-related source omitted] - Access to secretsline 703
[Credential-related source omitted] - Access to secretsline 972
[Credential-related source omitted] - Access to secretsline 973
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-binder-discovery/WORKFLOW_DETAILS.md
plugins/tooluniverse/skills/tooluniverse-bioinformatics-resource-discovery/SKILL.md
- Network accessline 48
Verified live: `SmartAPI_search_apis {"query": "biothings", "limit": 3}` -> real APIs including "Biothings Therapeutic Target Database API" (`base_url: https://biothings.transltr.io/ttd`) and "MyGeneset.info API". Feeding that `api_id` into
plugins/tooluniverse/skills/tooluniverse-biomedical-fact-lookup/SKILL.md
plugins/tooluniverse/skills/tooluniverse-cancer-genomics-tcga/SKILL.md
- Network accessline 302
offset = 0 while True: resp = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 303
while True: resp = requests.post("https://api.gdc.cancer.gov/cases", json={ "filters": filters, "size": 500, "from": offset, - Network accessline 304
resp = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 314
# Download MAF mutation file by UUID file_uuid = "abc123-..." # from GDC_list_files result url = f"https://api.gdc.cancer.gov/data/{file_uuid}" - Network accessline 315
file_uuid = "abc123-..." # from GDC_list_files result url = f"https://api.gdc.cancer.gov/data/{file_uuid}" content = requests.get(url, headers={"Content-Type": "application/json"}).content - Network accessline 316
url = f"https://api.gdc.cancer.gov/data/{file_uuid}" - Network accessline 317
content = requests.get(url, headers={"Content-Type": "application/json"}).content
plugins/tooluniverse/skills/tooluniverse-cancer-variant-interpretation/SKILL.md
- Access to secretsline 124
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-cell-line-profiling/SKILL.md
- Network accessline 137
**Per-cell-line Chronos scores** (what the API can't give you): use the bundled script `scripts/depmap_gene_dependency.py`. It pulls the current DepMap Public release (CRISPRGeneEffect.csv + Model.csv) once via the public download index, ca
plugins/tooluniverse/skills/tooluniverse-cell-line-profiling/scripts/depmap_gene_dependency.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 37
DEPMAP_INDEX = "https://depmap.org/portal/api/download/files" - Access to secretsline 38
[Credential-related source omitted] - Network accessline 49
with urllib.request.urlopen(DEPMAP_INDEX, timeout=60) as fh: - Network accessline 55
f"DepMap's download index ({DEPMAP_INDEX}) returned a web page instead of " f"the file list, so {filename} cannot be fetched automatically. Download " f"CRISPRGeneEffect.csv and Model.csv from https://depmap.org/port - Network accessline 56
f"the file list, so {filename} cannot be fetched automatically. Download " f"CRISPRGeneEffect.csv and Model.csv from https://depmap.org/portal/download/all/ " f"in a browser, put them in {CACHE_DIR} (or set DEPMAP_CA - Network accessline 57
f"CRISPRGeneEffect.csv and Model.csv from https://depmap.org/portal/download/all/ " - Network accessline 77
if not os.path.exists(path) or os.path.getsize(path) == 0: sys.stderr.write(f"Downloading {filename} (one-time; CRISPRGeneEffect is large)...\n") urllib.request.urlretrieve(_latest_url(filename), path) - Network accessline 78
sys.stderr.write(f"Downloading {filename} (one-time; CRISPRGeneEffect is large)...\n") urllib.request.urlretrieve(_latest_url(filename), path) return path - Network accessline 79
urllib.request.urlretrieve(_latest_url(filename), path)
plugins/tooluniverse/skills/tooluniverse-clinical-guidelines/SKILL.md
- Access to secretsline 124
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-clinical-guidelines/TOOLS_REFERENCE.md
- Access to secretsline 52
[Credential-related source omitted] - Network accessline 196
- **Returns**: dict with patient guideline text - **⚠<U+FE0F> CRITICAL**: Pass full URL, NOT integer ID - **Example**: `NCCN_get_patient_guideline(url='https://www.nccn.org/patientresources/patient-resources/guidelines-for-patients/guidelines-for- - Network accessline 198
- **Example**: `NCCN_get_patient_guideline(url='https://www.nccn.org/patientresources/patient-resources/guidelines-for-patients/guidelines-for-patients-details?patientGuidelineId=61')`
plugins/tooluniverse/skills/tooluniverse-clinical-terminology-lookup/SKILL.md
- Access to secretsline 124
[Credential-related source omitted] - Network accessline 422
"22298006"}` -> `"Myocardial infarction"`. The standout use is subsumption expansion: `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006"}` returns every clinical - Network accessline 423
subsumption expansion: `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006"}` returns every clinical subtype (verified live: "Old myocardial infarction", "Acute myocardial - Network accessline 424
"http://snomed.info/sct?fhir_vs=isa/22298006"}` returns every clinical
plugins/tooluniverse/skills/tooluniverse-clinical-terminology-lookup/references/clinical_tables_tool_reference.md
- Network accessline 202
"error", "error": "ICD API authentication required. Set ICD_CLIENT_ID and ICD_CLIENT_SECRET environment variables. Register at: https://icd.who.int/icdapi"}`. Same for `ICD11_get_entity` (2/2 failed) - Network accessline 203
[Credential-related source omitted] - Access to secretsline 203
[Credential-related source omitted] - Network accessline 204
https://icd.who.int/icdapi"}`. Same for `ICD11_get_entity` (2/2 failed) - Network accessline 214
for a user is the same either way (a clear error, no fabricated data), just via a different code path than the more common gating pattern. Register free at https://icd.who.int/icdapi to use these. - Network accessline 216
Register free at https://icd.who.int/icdapi to use these. - Network accessline 361
All 4 tools type `BioPortalTool`, no API key required. `get_concept`/ `get_hierarchy` need a full IRI (`http://purl.obolibrary.org/obo/DOID_1909` - Network accessline 362
`get_hierarchy` need a full IRI (`http://purl.obolibrary.org/obo/DOID_1909` - Network accessline 380
{"matched_text": "HYPERTENSION", "from": 23, "to": 34, "match_type": "PREF", "concept_label": "hypertension", "concept_id": "DOID_10763", "concept_full_id": "http://purl.obolibrary.org/obo/DOID_10763"}, - Network accessline 381
"concept_label": "hypertension", "concept_id": "DOID_10763", "concept_full_id": "http://purl.obolibrary.org/obo/DOID_10763"}, {"matched_text": "TYPE 2 DIABETES MELLITUS", "from": 40, "to": 63, "match_type": "PREF", - Network accessline 382
"concept_full_id": "http://purl.obolibrary.org/obo/DOID_10763"}, - Network accessline 385
{"matched_text": "METFORMIN", "from": 77, "to": 85, "match_type": "PREF", "concept_label": "metformin", "concept_id": "6809", "concept_full_id": "http://purl.bioontology.org/ontology/RXNORM/6809"} - Network accessline 386
"concept_label": "metformin", "concept_id": "6809", "concept_full_id": "http://purl.bioontology.org/ontology/RXNORM/6809"} ], "metadata": {"source": "BioPortal Annotator (NCBO)", "total_annotations": 3, "text_length": 86}} - Network accessline 387
"concept_full_id": "http://purl.bioontology.org/ontology/RXNORM/6809"} - Network accessline 404
or a full FHIR system URI), `code` (required). `tu test` PASS, 2/2: `{"system": "snomed", "code": "22298006"}` -> `{"code": "22298006", "display": "Myocardial infarction", "system": "http://snomed.info/sct"}`; - Network accessline 405
`{"system": "snomed", "code": "22298006"}` -> `{"code": "22298006", "display": "Myocardial infarction", "system": "http://snomed.info/sct"}`; `{"system": "snomed", "code": "73211009"}` -> `"Diabetes mellitus"`. - Network accessline 406
"display": "Myocardial infarction", "system": "http://snomed.info/sct"}`; - Network accessline 408
- **`FHIRTerminology_expand_valueset`** — `value_set_url` (required), `limit` (int, 1-500, default 50). `tu test` PASS, 2/2. **Custom call** `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006", "limit": 5}` - Network accessline 409
`limit` (int, 1-500, default 50). `tu test` PASS, 2/2. **Custom call** `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006", "limit": 5}` -> real subtype list: `1755008` ("Old myocardial infarction"), - Network accessline 410
`{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006", "limit": 5}` - Network accessline 411
-> real subtype list: `1755008` ("Old myocardial infarction"), `15990001` ("Acute myocardial infarction of posterolateral wall"), plus more, all sharing `"system": "http://snomed.info/sct"`. Use the - Network accessline 412
`15990001` ("Acute myocardial infarction of posterolateral wall"), plus more, all sharing `"system": "http://snomed.info/sct"`. Use the `isa/<code>` value-set URL pattern for "every descendant of this SNOMED - Network accessline 413
more, all sharing `"system": "http://snomed.info/sct"`. Use the
plugins/tooluniverse/skills/tooluniverse-clinical-trial-design/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-clinical-trial-design/SKILL.md
- Network accessline 233
[Credential-related source omitted] - Network accessline 234
[Credential-related source omitted] - Network accessline 235
resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json() - Network accessline 252
# FDA drug approval history drug = "pembrolizumab" fda = requests.get(f"https://api.fda.gov/drug/drugsfda.json?search=openfda.brand_name:{drug}&limit=10").json() - Network accessline 254
fda = requests.get(f"https://api.fda.gov/drug/drugsfda.json?search=openfda.brand_name:{drug}&limit=10").json()
plugins/tooluniverse/skills/tooluniverse-clinical-trial-design/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-clinical-trial-design/trial_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-clinical-trial-matching/QUICK_START.md
- Network accessline 136
- Trial availability changes frequently; always verify current status at [ClinicalTrials.gov](https://clinicaltrials.gov)
plugins/tooluniverse/skills/tooluniverse-clinical-trial-matching/REPORT_TEMPLATE.md
- Network accessline 178
This report is for informational and research purposes only. Clinical trial eligibility is ultimately determined by the trial investigators based on complete medical records. Patients should discuss all options with their healthcare team. T
plugins/tooluniverse/skills/tooluniverse-clinical-trial-protocol/scripts/sample_size_calculator.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-codex-plugin/SKILL.md
- Dangerous shell operationline 14
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 14
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 15
codex --version # OpenAI Codex CLI; if missing: https://developers.openai.com/codex - Access to secretsline 62
[Credential-related source omitted] - Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 64
[Credential-related source omitted] - Network accessline 99
Replace `1.3.0` with any release from https://pypi.org/project/tooluniverse/, then restart Codex. (Find the file under `~/.codex/plugins/cache/`.) - Network accessline 111
git clone https://github.com/mims-harvard/ToolUniverse.git - Network accessline 131
Still stuck: https://github.com/mims-harvard/ToolUniverse/issues - Network accessline 138
# 1. Is uv/uvx installed? (most common cause of "no tools at all") command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Dangerous shell operationline 139
command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Network accessline 139
command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh"
plugins/tooluniverse/skills/tooluniverse-comparative-genomics/scripts/dnds.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/scripts/burn_fluids.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/scripts/env_risk_assessment.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/scripts/enzyme_kinetics.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/scripts/epidemiology.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/scripts/fluid_calculations.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/scripts/herd_immunity.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/scripts/iv_drip_rate.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/scripts/mc_analyzer.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-computational-biophysics/scripts/radioactive_decay.py
plugins/tooluniverse/skills/tooluniverse-connect/SKILL.md
- Network accessline 15
| What you see | The user is on | |---|---| | ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which - Network accessline 16
|---|---| | ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which still works) | **ToolUniverse Con - Network accessline 17
| ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which still works) | **ToolUniverse Connect** (ho - Network accessline 37
The user needs a free account: https://connect.aiscientist.tools/register. Its **Your AI scientist** page (https://connect.aiscientist.tools/connect-agent) shows these steps for each app. - Network accessline 41
- **Claude (claude.ai or the desktop app):** **Customize → Connectors**, click **+ Add**, then **Add custom connector**. Name it ToolUniverse, URL `https://mcp.aiscientist.tools`. Sign in and click **Allow**. In each new chat, click **+** u - Network accessline 42
- **Claude Code:** run `claude mcp add --transport http --scope user tooluniverse "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- claude-code && claude mcp login tooluniverse` A brows - Dangerous shell operationline 43
`claude mcp add --transport http --scope user tooluniverse "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- claude-code && claude mcp login tooluniverse` - Network accessline 43
`claude mcp add --transport http --scope user tooluniverse "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- claude-code && claude mcp login tooluniverse` - Network accessline 44
A browser page opens; the user signs in and clicks **Allow**. Then start a new Claude Code session. - **Codex:** run `codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills. - Network accessline 45
- **Codex:** run `codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- codex` It starts the browser sign-in; if no page opens, run `codex mcp login tooluni - Dangerous shell operationline 46
`codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- codex` - Network accessline 46
`codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- codex` - Network accessline 59
[Credential-related source omitted] - Access to secretsline 59
[Credential-related source omitted] - Access to secretsline 60
[Credential-related source omitted] - Network accessline 78
- **No ToolUniverse tools in this chat:** the app is not connected, or in Claude the connector is off for this chat. Follow section 3. - **Asked to sign in again, or "unauthorized":** Claude Code: `claude mcp login tooluniverse`. Codex: `co - Network accessline 79
- **Asked to sign in again, or "unauthorized":** Claude Code: `claude mcp login tooluniverse`. Codex: `codex mcp login tooluniverse`. Claude: **Customize → Connectors**, open ToolUniverse and connect again. A password reset signs out every - Network accessline 80
- **"Starting" or "unavailable" right after an update:** the service is restarting. Wait a minute and try again. Live status: https://connect.aiscientist.tools/status. - Network accessline 85
Privacy, in one line if asked: the user's conversation stays with their AI app's provider; Connect receives only the tool requests the AI app sends. Details: https://connect.aiscientist.tools/privacy.
plugins/tooluniverse/skills/tooluniverse-crispr-screen-analysis/scripts/reference_gene_sets.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-custom-tool/SKILL.md
- Network accessline 41
"type": "BaseRESTTool", "fields": { "endpoint": "https://my-api.example.com/search" - Network accessline 42
"fields": { "endpoint": "https://my-api.example.com/search" }, - Network accessline 43
"endpoint": "https://my-api.example.com/search" - Network accessline 87
def run(self, q: str, limit: int = 10) -> dict: import requests resp = requests.get( - Network accessline 88
import requests resp = requests.get( "https://my-api.example.com/search", - Network accessline 89
resp = requests.get( - Network accessline 90
"https://my-api.example.com/search", - Network accessline 188
return {"error": "query parameter is required"} try: resp = requests.get( - Network accessline 189
try: resp = requests.get( "https://my-api.example.com/search", - Network accessline 190
resp = requests.get( - Network accessline 191
"https://my-api.example.com/search",
plugins/tooluniverse/skills/tooluniverse-custom-tool/references/json-tool.md
- Network accessline 9
"description": "...", "type": "BaseRESTTool", "fields": { "endpoint": "https://api.example.com/search" }, - Network accessline 10
"type": "BaseRESTTool", "fields": { "endpoint": "https://api.example.com/search" }, "parameter": { - Network accessline 11
"fields": { "endpoint": "https://api.example.com/search" }, - Network accessline 71
"type": "BaseRESTTool", "fields": { "endpoint": "https://api.example.com/jobs", - Network accessline 72
"fields": { "endpoint": "https://api.example.com/jobs", "method": "POST", - Network accessline 73
"endpoint": "https://api.example.com/jobs", - Network accessline 97
"fields": { "endpoint": "https://api.example.com/search", - Network accessline 98
"endpoint": "https://api.example.com/search", - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 111
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Network accessline 114
"domain": "Drugs & Chemistry", "type": "secret", "register_url": "https://example.com/get-a-key", - Network accessline 115
"type": "secret", "register_url": "https://example.com/get-a-key", "purpose": "What this key unlocks (one sentence).", - Network accessline 116
"register_url": "https://example.com/get-a-key", - Access to secretsline 135
[Credential-related source omitted] - Network accessline 215
import urllib.request, json with urllib.request.urlopen("https://api.example.com/search?q=test") as r: - Network accessline 216
with urllib.request.urlopen("https://api.example.com/search?q=test") as r: - Network accessline 225
**Verify the URL is a real JSON endpoint.** Some URLs that look like REST APIs (e.g., `https://certification.example.org/api/projects`) may redirect to a static HTML page. A urllib fetch will show you the Content-Type and body immediately, - Network accessline 226
(e.g., `https://certification.example.org/api/projects`) may redirect to a static HTML page.
plugins/tooluniverse/skills/tooluniverse-custom-tool/references/python-tool.md
- Network accessline 27
def run(self, q: str, limit: int = 10) -> dict: import requests resp = requests.get( - Network accessline 28
import requests resp = requests.get( "https://my-api.example.com/search", - Network accessline 29
resp = requests.get( - Network accessline 30
"https://my-api.example.com/search", - Network accessline 68
return {"error": "query parameter is required"} try: resp = requests.get( - Network accessline 69
try: resp = requests.get( "https://my-api.example.com/search", - Network accessline 70
resp = requests.get( - Network accessline 71
"https://my-api.example.com/search", - Network accessline 223
if _cache is None: import requests resp = requests.get("https://api.example.com/full-index", timeout=timeout) - Network accessline 224
import requests resp = requests.get("https://api.example.com/full-index", timeout=timeout) resp.raise_for_status() - Network accessline 225
resp = requests.get("https://api.example.com/full-index", timeout=timeout) - Access to secretsline 248
[Credential-related source omitted] - Access to secretsline 249
[Credential-related source omitted] - Access to secretsline 250
[Credential-related source omitted] - Network accessline 299
import requests try: resp = requests.get("https://api.example.com/search", params={"q": q}, timeout=30) - Network accessline 300
try: resp = requests.get("https://api.example.com/search", params={"q": q}, timeout=30) resp.raise_for_status() - Network accessline 301
resp = requests.get("https://api.example.com/search", params={"q": q}, timeout=30)
plugins/tooluniverse/skills/tooluniverse-data-wrangling/SKILL.md
- Network accessline 62
import pysam # BAM (requires pysam) bam = pysam.AlignmentFile("aligned.bam", "rb") for read in bam.fetch("chr1", 1000, 2000): print(read.query_name) - Network accessline 64
for read in bam.fetch("chr1", 1000, 2000): print(read.query_name) - Network accessline 164
import requests base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 165
base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" # Search -> get IDs -> fetch records in batches ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA1+AND+human&retmax=500&retmode=json").json() - Network accessline 166
# Search -> get IDs -> fetch records in batches ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA1+AND+human&retmax=500&retmode=json").json() id_list = ids["esearchresult"]["idlist"] - Network accessline 167
ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA1+AND+human&retmax=500&retmode=json").json() - Network accessline 170
for i in range(0, len(id_list), 500): batch = ",".join(id_list[i:i+500]) data = requests.get(f"{base}/efetch.fcgi?db=gene&id={batch}&retmode=xml").text - Network accessline 172
data = requests.get(f"{base}/efetch.fcgi?db=gene&id={batch}&retmode=xml").text - Network accessline 178
# UniProt bulk TSV download with cursor pagination url = "https://rest.uniprot.org/uniprotkb/search?query=organism_id:9606+AND+keyword:kinase&format=tsv&size=500" - Network accessline 179
url = "https://rest.uniprot.org/uniprotkb/search?query=organism_id:9606+AND+keyword:kinase&format=tsv&size=500" - Network accessline 180
all_rows = [] while url: resp = requests.get(url) - Network accessline 181
while url: resp = requests.get(url) all_rows.append(resp.text) - Network accessline 182
resp = requests.get(url) - Network accessline 193
{"op":"=","content":{"field":"demographic.vital_status","value":"Dead"}} ]} cases = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 194
]} cases = requests.post("https://api.gdc.cancer.gov/cases", json={ "filters": filters, "fields": "demographic.vital_status,diagnoses.days_to_death", - Network accessline 195
cases = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 204
# Direct NHANES XPT download (any cycle, any component) cycle, component = "2017-2018", "DEMO_J" url = f"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/{component}.XPT" - Network accessline 205
cycle, component = "2017-2018", "DEMO_J" url = f"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/{component}.XPT" df = pd.read_sas(io.BytesIO(requests.get(url).content), format="xport") - Network accessline 206
url = f"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/{component}.XPT" - Network accessline 207
df = pd.read_sas(io.BytesIO(requests.get(url).content), format="xport") - Network accessline 213
# GWAS Catalog full download (37MB TSV, all associations) url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 214
url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 223
# PubChem batch property retrieval (up to 100 CIDs at once) cids = "2244,5988,3672" # aspirin, sucrose, ibuprofen url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA/JSON" - Network accessline 224
cids = "2244,5988,3672" # aspirin, sucrose, ibuprofen url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA/JSON" props = requests.get(url).json()["PropertyTable"]["Properties"] - Network accessline 225
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA/JSON" - Network accessline 226
props = requests.get(url).json()["PropertyTable"]["Properties"] - Network accessline 232
# GEO series matrix direct download geo_id = "GSE12345" url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{geo_id[:5]}nnn/{geo_id}/matrix/{geo_id}_series_matrix.txt.gz" - Network accessline 233
geo_id = "GSE12345" url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{geo_id[:5]}nnn/{geo_id}/matrix/{geo_id}_series_matrix.txt.gz" df = pd.read_csv(url, sep="\t", comment="!", index_col=0) - Network accessline 234
url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{geo_id[:5]}nnn/{geo_id}/matrix/{geo_id}_series_matrix.txt.gz" - Network accessline 237
# GTEx bulk expression (median TPM per tissue) url = "https://storage.googleapis.com/adult-gtex/bulk-gex/v8/rna-seq/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.gz" - Network accessline 238
url = "https://storage.googleapis.com/adult-gtex/bulk-gex/v8/rna-seq/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.gz" - Network accessline 249
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resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json() - Network accessline 262
all_results = [] while cursor: resp = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 263
while cursor: resp = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", params={"query": "BRCA1 AND resistance", "format": "json", "pageSize": 100, "cursorMark": cursor}).json() - Network accessline 264
resp = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 273
# Zenodo: search + download files record = requests.get("https://zenodo.org/api/records", params={"q": "proteomics cancer", "size": 5}).json()["hits"]["hits"][0] - Network accessline 274
record = requests.get("https://zenodo.org/api/records", params={"q": "proteomics cancer", "size": 5}).json()["hits"]["hits"][0] - Network accessline 276
content = requests.get(f["links"]["self"]).content # download each file - Network accessline 335
offset = 0 while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30).json() - Network accessline 336
while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30).json() batch = resp.get("data", resp.get("results", resp.get("hits", []))) - Network accessline 337
resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30).json() - Network accessline 346
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resp = requests.get(api_url, params=params).json() - Network accessline 357
def fetch_with_retry(url, max_retries=3, **kwargs): for attempt in range(max_retries): resp = requests.get(url, timeout=30, **kwargs) - Network accessline 358
for attempt in range(max_retries): resp = requests.get(url, timeout=30, **kwargs) if resp.status_code == 200: return resp - Network accessline 359
resp = requests.get(url, timeout=30, **kwargs) - Access to secretsline 373
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def download_large_file(url, output_path): with requests.get(url, stream=True, timeout=300) as r: - Network accessline 382
with requests.get(url, stream=True, timeout=300) as r: - Network accessline 391
resp = requests.get(url, timeout=30)
plugins/tooluniverse/skills/tooluniverse-data-wrangling/references/specialized-domains.md
- Network accessline 26
# PRIDE project files — search then download raw/processed data project = requests.get("https://www.ebi.ac.uk/pride/ws/archive/v2/projects/PXD012345").json() - Network accessline 27
project = requests.get("https://www.ebi.ac.uk/pride/ws/archive/v2/projects/PXD012345").json() - Network accessline 28
files = requests.get(f"https://www.ebi.ac.uk/pride/ws/archive/v2/projects/PXD012345/files").json() - Network accessline 33
# ProteomeXchange: search across PRIDE + MassIVE + jPOST px = requests.get("https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD012345&outputMode=JSON").json() - Network accessline 34
px = requests.get("https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD012345&outputMode=JSON").json() - Network accessline 40
# MetaboLights study download study_id = "MTBLS1234" study = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}").json() - Network accessline 41
study_id = "MTBLS1234" study = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}").json() # Download metabolite assignment file - Network accessline 42
study = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}").json() - Network accessline 44
files = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}/files").json() - Network accessline 46
# Metabolomics Workbench REST mw = requests.get("https://www.metabolomicsworkbench.org/rest/study/study_id/ST001234/analysis").json() - Network accessline 47
mw = requests.get("https://www.metabolomicsworkbench.org/rest/study/study_id/ST001234/analysis").json() - Network accessline 49
# HMDB metabolite data hmdb = requests.get("https://hmdb.ca/metabolites/HMDB0000001.xml").text # XML format - Network accessline 50
hmdb = requests.get("https://hmdb.ca/metabolites/HMDB0000001.xml").text # XML format - Network accessline 56
# MGnify: search analyses, download taxonomy/function profiles analyses = requests.get("https://www.ebi.ac.uk/metagenomics/api/v1/analyses", - Network accessline 57
analyses = requests.get("https://www.ebi.ac.uk/metagenomics/api/v1/analyses", - Network accessline 59
# Download OTU/taxonomy TSV for a in analyses["data"]: tax_url = f"https://www.ebi.ac.uk/metagenomics/api/v1/analyses/{a['id']}/downloads" - Network accessline 60
for a in analyses["data"]: tax_url = f"https://www.ebi.ac.uk/metagenomics/api/v1/analyses/{a['id']}/downloads" downloads = requests.get(tax_url).json() - Network accessline 61
tax_url = f"https://www.ebi.ac.uk/metagenomics/api/v1/analyses/{a['id']}/downloads" - Network accessline 62
downloads = requests.get(tax_url).json() - Network accessline 64
# GMREPO: gut microbiome phenotype associations gmrepo = requests.get("https://gmrepo.humangut.info/api/getAssociatedSpeciesByMeshID", - Network accessline 65
gmrepo = requests.get("https://gmrepo.humangut.info/api/getAssociatedSpeciesByMeshID", - Network accessline 74
offset = 0 while True: resp = requests.get("https://api.gbif.org/v1/occurrence/search", - Network accessline 75
while True: resp = requests.get("https://api.gbif.org/v1/occurrence/search", params={"scientificName": "Panthera tigris", "limit": 300, "offset": offset}).json() - Network accessline 76
resp = requests.get("https://api.gbif.org/v1/occurrence/search", - Network accessline 82
# iNaturalist observations obs = requests.get("https://api.inaturalist.org/v1/observations", - Network accessline 83
obs = requests.get("https://api.inaturalist.org/v1/observations", - Network accessline 90
# FlyBase gene data fb = requests.get("https://api.flybase.org/api/v1.0/gene/FBgn0000490").json() # dpp gene - Network accessline 91
fb = requests.get("https://api.flybase.org/api/v1.0/gene/FBgn0000490").json() # dpp gene - Network accessline 93
# WormBase gene info wb = requests.get("https://wormbase.org/rest/widget/gene/WBGene00006763/overview", - Network accessline 94
wb = requests.get("https://wormbase.org/rest/widget/gene/WBGene00006763/overview", - Network accessline 97
# ZFIN (zebrafish) gene expression zfin = requests.get("https://zfin.org/action/api/marker/ZDB-GENE-980526-166/expression").json() - Network accessline 98
zfin = requests.get("https://zfin.org/action/api/marker/ZDB-GENE-980526-166/expression").json() - Network accessline 100
# RGD (rat) — disease annotations for a gene rgd = requests.get("https://rest.rgd.mcw.edu/rgdws/genes/Tp53/9606").json() # human TP53 - Network accessline 101
rgd = requests.get("https://rest.rgd.mcw.edu/rgdws/genes/Tp53/9606").json() # human TP53 - Network accessline 107
# Reactome pathway participants pathway_id = "R-HSA-109582" # Hemostasis participants = requests.get(f"https://reactome.org/ContentService/data/participants/{pathway_id}").json() - Network accessline 109
participants = requests.get(f"https://reactome.org/ContentService/data/participants/{pathway_id}").json() - Network accessline 111
# STRING protein-protein interactions (bulk) proteins = "9606.ENSP00000269305%0d9606.ENSP00000344818" # TP53, MDM2 network = requests.get(f"https://string-db.org/api/json/network?identifiers={proteins}&species=9606").json() - Network accessline 113
network = requests.get(f"https://string-db.org/api/json/network?identifiers={proteins}&species=9606").json() - Network accessline 115
# BioGRID interactions for a gene (tab-delimited bulk) url = "https://webservice.thebiogrid.org/interactions/?searchNames=true&geneList=BRCA1&taxId=9606&format=json&accesskey=YOUR_KEY" - Network accessline 116
url = "https://webservice.thebiogrid.org/interactions/?searchNames=true&geneList=BRCA1&taxId=9606&format=json&accesskey=YOUR_KEY" - Network accessline 118
# WikiPathways: download pathway as GPML or GMT gpml = requests.get("https://www.wikipathways.org/wikipathways/wpi/wpi.php?action=downloadFile&type=gpml&pwTitle=Pathway:WP254").text - Network accessline 119
gpml = requests.get("https://www.wikipathways.org/wikipathways/wpi/wpi.php?action=downloadFile&type=gpml&pwTitle=Pathway:WP254").text - Network accessline 125
# OLS term search + hierarchy traversal terms = requests.get("https://www.ebi.ac.uk/ols4/api/search", - Network accessline 126
terms = requests.get("https://www.ebi.ac.uk/ols4/api/search", - Network accessline 129
# Get children of a GO term children = requests.get("https://www.ebi.ac.uk/ols4/api/ontologies/go/terms/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FGO_0006915/children").json() - Network accessline 130
children = requests.get("https://www.ebi.ac.uk/ols4/api/ontologies/go/terms/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FGO_0006915/children").json() - Network accessline 132
# HPO: phenotype-to-disease annotations hpo = requests.get("https://hpo.jax.org/api/hpo/term/HP:0001250/diseases").json() - Network accessline 133
hpo = requests.get("https://hpo.jax.org/api/hpo/term/HP:0001250/diseases").json() - Network accessline 135
# Gene Ontology annotations bulk (GAF format) # Download from: http://current.geneontology.org/annotations/goa_human.gaf.gz - Network accessline 136
# Download from: http://current.geneontology.org/annotations/goa_human.gaf.gz - Network accessline 142
# IEDB epitope search epitopes = requests.get("https://query-api.iedb.org/epitope_search", - Network accessline 143
epitopes = requests.get("https://query-api.iedb.org/epitope_search", - Network accessline 146
# VDJdb: T-cell receptor specificity database vdjdb = pd.read_csv("https://raw.githubusercontent.com/antigenomics/vdjdb-db/master/latest-version.zip", - Network accessline 147
vdjdb = pd.read_csv("https://raw.githubusercontent.com/antigenomics/vdjdb-db/master/latest-version.zip", - Network accessline 150
# ImmPort shared data (requires registration) # Search at: https://www.immport.org/shared/search - Network accessline 151
# Search at: https://www.immport.org/shared/search - Network accessline 157
# DGIdb drug-gene interactions dgi = requests.get("https://dgidb.org/api/v2/interactions.json", - Network accessline 158
dgi = requests.get("https://dgidb.org/api/v2/interactions.json", - Network accessline 161
# PharmGKB clinical annotations for a gene pgkb = requests.get("https://api.pharmgkb.org/v1/data/clinicalAnnotation", - Network accessline 162
pgkb = requests.get("https://api.pharmgkb.org/v1/data/clinicalAnnotation", - Network accessline 165
# SIDER side effect frequencies (bulk download) # Download from: http://sideeffects.embl.de/media/download/meddra_freq.tsv.gz - Network accessline 166
# Download from: http://sideeffects.embl.de/media/download/meddra_freq.tsv.gz - Network accessline 172
# TCIA: cancer imaging collections collections = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getCollectionValues").json() - Network accessline 173
collections = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getCollectionValues").json() - Network accessline 174
# Get series for a patient series = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getSeries", params={"Collection": "TCGA-BRCA", "PatientID": "TCGA-A1-A0SB"}).json() - Network accessline 175
series = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getSeries", - Network accessline 178
# Human Protein Atlas: tissue expression hpa = requests.get("https://www.proteinatlas.org/api/search_download.php?search=TP53&format=json&columns=g,t,up").json() - Network accessline 179
hpa = requests.get("https://www.proteinatlas.org/api/search_download.php?search=TP53&format=json&columns=g,t,up").json() - Network accessline 181
# Allen Brain Atlas: gene expression in brain regions aba = requests.get("https://api.brain-map.org/api/v2/data/query.json?criteria=model::Gene,rma::criteria,[acronym$eq'BDNF']").json() - Network accessline 182
aba = requests.get("https://api.brain-map.org/api/v2/data/query.json?criteria=model::Gene,rma::criteria,[acronym$eq'BDNF']").json() - Network accessline 188
# Download PDB/CIF structure files directly pdb_id = "1A2B" pdb_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.pdb").text - Network accessline 189
pdb_id = "1A2B" pdb_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.pdb").text cif_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif").text - Network accessline 190
pdb_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.pdb").text - Network accessline 191
cif_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif").text - Network accessline 193
# AlphaFold predicted structure by UniProt ID uniprot_id = "P04637" # TP53 af_pdb = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb").text - Network accessline 194
uniprot_id = "P04637" # TP53 af_pdb = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb").text af_cif = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.cif").text - Network accessline 195
af_pdb = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb").text - Network accessline 196
af_cif = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.cif").text - Network accessline 198
# RCSB advanced search (GraphQL) for bulk queries query = {"query": {"type": "terminal", "service": "text", "parameters": {"attribute": "rcsb_entity_source_organism.taxonomy_lineage.name", "operator": "exact_match", "value": "Homo sapiens"} - Network accessline 200
results = requests.post("https://search.rcsb.org/rcsbsearch/v2/query", json=query).json() - Network accessline 206
# ClinVar bulk download (variant_summary, ~200MB) # df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") - Network accessline 207
# df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") - Network accessline 209
# CIViC GraphQL API — all evidence items for a gene query = '{"query": "{ gene(entrezId: 673) { name variants { nodes { name evidenceItems { nodes { description evidenceLevel } } } } } }"}' civic = requests.post("https://civicdb.org/api/gra - Network accessline 211
civic = requests.post("https://civicdb.org/api/graphql", json={"query": query}).json() - Network accessline 213
# ClinGen allele registry allele = requests.get("https://reg.clinicalgenome.org/allele?hgvs=NM_000059.4:c.68_69del").json() - Network accessline 214
allele = requests.get("https://reg.clinicalgenome.org/allele?hgvs=NM_000059.4:c.68_69del").json() - Network accessline 226
# ARCHS4 gene expression (pre-computed from SRA) archs4 = requests.get("https://maayanlab.cloud/archs4/search/loadExpressionTSV.php", - Network accessline 227
archs4 = requests.get("https://maayanlab.cloud/archs4/search/loadExpressionTSV.php", - Network accessline 230
# Without cellxgene_census: download h5ad directly from cellxgene data portal # Browse collections at https://cellxgene.cziscience.com/collections - Network accessline 231
# Browse collections at https://cellxgene.cziscience.com/collections - Network accessline 237
# CTD: chemical-gene-disease interactions ctd = requests.get("https://ctdbase.org/tools/batchQuery.go", - Network accessline 238
ctd = requests.get("https://ctdbase.org/tools/batchQuery.go", - Network accessline 241
# EPA CompTox Dashboard comptox = requests.get("https://comptox.epa.gov/dashboard/api/search/chemical/equal/Bisphenol%20A").json() - Network accessline 242
comptox = requests.get("https://comptox.epa.gov/dashboard/api/search/chemical/equal/Bisphenol%20A").json()
plugins/tooluniverse/skills/tooluniverse-database-logins/SKILL.md
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| Login | Unlocks | Where to get it | Cost / wait | |---|---|---|---| | `NCBI_API_KEY` (+ `NCBI_EMAIL`) | Faster PubMed, Gene, Protein and other NCBI tools (they work without it, slower) | https://account.ncbi.nlm.nih.gov/settings/ → API Ke - Network accessline 21
|---|---|---|---| | `NCBI_API_KEY` (+ `NCBI_EMAIL`) | Faster PubMed, Gene, Protein and other NCBI tools (they work without it, slower) | https://account.ncbi.nlm.nih.gov/settings/ → API Key Management | Free, ~2 minutes | | `OMIM_API_KEY` | - Network accessline 22
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| `HF_TOKEN` | Hugging Face–hosted models and datasets | https://huggingface.co/settings/tokens | Free account | - Network accessline 30
The full list, with steps for each, is on https://connect.aiscientist.tools/credentials (search box). Before the user signs up for anything paid, metered, or requiring a license or an institutional claim, say so and let them decide. - Network accessline 40
1. Open https://connect.aiscientist.tools/credentials. - Access to secretsline 45
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plugins/tooluniverse/skills/tooluniverse-dataset-discovery/SKILL.md
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# --- Download from URL first, then parse --- resp = requests.get(url, timeout=120) - Network accessline 111
resp = requests.get(url, timeout=120) - Network accessline 129
offset = 0 while True: resp = requests.get(f"{api_url}?offset={offset}&limit=100", timeout=30) - Network accessline 130
while True: resp = requests.get(f"{api_url}?offset={offset}&limit=100", timeout=30) batch = resp.json().get("data", []) - Network accessline 131
resp = requests.get(f"{api_url}?offset={offset}&limit=100", timeout=30)
plugins/tooluniverse/skills/tooluniverse-diagnostic-test-evaluation/scripts/roc_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-disease-research/SKILL.md
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"max_results": 3, }) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 78
"max_results": 3, }) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 79
}) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], # "curies": ["MONDO:0004979"], "confidence": "HIGH", "source": "zooma", ...}] - Network accessline 79
}) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], # "curies": ["MONDO:0004979"], "confidence": "HIGH", "source": "zooma", ...}] - Network accessline 80
# -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 80
# -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 86
# Inspect which curated datasources back ZOOMA annotations (for provenance): tu.run_tool("ZOOMA_list_datasources", {}) # -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...] - Network accessline 86
# Inspect which curated datasources back ZOOMA annotations (for provenance): tu.run_tool("ZOOMA_list_datasources", {}) # -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...] - Network accessline 88
# -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...] - Network accessline 88
# -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...]
plugins/tooluniverse/skills/tooluniverse-disease-research/TOOLS_REFERENCE.md
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plugins/tooluniverse/skills/tooluniverse-dose-response/scripts/fit_dose_response.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-drug-drug-interaction/.env.template
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plugins/tooluniverse/skills/tooluniverse-drug-drug-interaction/ddi_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-drug-drug-interaction/ddi_working_example.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-drug-drug-interaction/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py
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Executable file
plugins/tooluniverse/skills/tooluniverse-drug-repurposing/REFERENCE.md
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- **ToolUniverse Documentation**: https://zitniklab.hms.harvard.edu/ToolUniverse/ - Network accessline 670
- **Tool Catalog**: https://zitniklab.hms.harvard.edu/ToolUniverse/tools/ - Network accessline 671
- **DrugBank**: https://go.drugbank.com/ - Network accessline 672
- **OpenTargets**: https://platform.opentargets.org/ - Network accessline 673
- **ChEMBL**: https://www.ebi.ac.uk/chembl/ - Network accessline 674
- **OpenFDA**: https://open.fda.gov/ - Network accessline 675
- **PubMed**: https://pubmed.ncbi.nlm.nih.gov/
plugins/tooluniverse/skills/tooluniverse-drug-repurposing/SKILL.md
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| Disease not found | Try synonyms or EFO ID lookup | | No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key st - Network accessline 280
| Disease not found | Try synonyms or EFO ID lookup | | No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key st - Network accessline 281
| No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key still activating — verify at https://data.uspto.gov/myod - Network accessline 281
| No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key still activating — verify at https://data.uspto.gov/myod - Network accessline 282
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plugins/tooluniverse/skills/tooluniverse-drug-research/EXAMPLES.md
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| Identifier | Value | Link | |------------|-------|------| | **PubChem CID** | 4091 | [Link](https://pubchem.ncbi.nlm.nih.gov/compound/4091) | - Network accessline 39
|------------|-------|------| | **PubChem CID** | 4091 | [Link](https://pubchem.ncbi.nlm.nih.gov/compound/4091) | | **ChEMBL ID** | CHEMBL1431 | [Link](https://www.ebi.ac.uk/chembl/compound_report_card/CHEMBL1431/) | - Network accessline 40
| **PubChem CID** | 4091 | [Link](https://pubchem.ncbi.nlm.nih.gov/compound/4091) | - Network accessline 41
| **ChEMBL ID** | CHEMBL1431 | [Link](https://www.ebi.ac.uk/chembl/compound_report_card/CHEMBL1431/) | - Network accessline 42
| **RxNorm CUI** | 6809 | RxNorm | | **PharmGKB ID** | PA450657 | [Link](https://www.pharmgkb.org/chemical/PA450657) | | **DailyMed** | Multiple | FDA approved | - Network accessline 43
| **PharmGKB ID** | PA450657 | [Link](https://www.pharmgkb.org/chemical/PA450657) |
plugins/tooluniverse/skills/tooluniverse-drug-synergy/scripts/synergy_reference.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-ecology-biodiversity/SKILL.md
- Access to secretsline 57
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-electron-microscopy/SKILL.md
- Network accessline 276
IDR (Image Data Resource, https://idr.openmicroscopy.org) is the reference repository for *light* microscopy and high-content screening data — distinct from the EM databases above. Use it to find published imaging studies and images by cura
plugins/tooluniverse/skills/tooluniverse-enzyme-kinetics/scripts/fit_michaelis_menten.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-epidemiological-analysis/SKILL.md
- Network accessline 68
# Generic download helper — adapt URL and format to your source def download_and_parse(url, fmt="csv"): r = requests.get(url, timeout=120) - Network accessline 69
def download_and_parse(url, fmt="csv"): r = requests.get(url, timeout=120) content = io.BytesIO(r.content) - Network accessline 70
r = requests.get(url, timeout=120) - Access to secretsline 111
[Credential-related source omitted] - Network accessline 145
offset = 0 while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30) - Network accessline 146
while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30) batch = resp.json().get("data", []) - Network accessline 147
resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30)
plugins/tooluniverse/skills/tooluniverse-epigenomics/.env.template
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plugins/tooluniverse/skills/tooluniverse-epigenomics/scripts/methylation_density.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-fastq-qc/scripts/run_fastq_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-gene-disease-association/SKILL.md
plugins/tooluniverse/skills/tooluniverse-gene-enrichment/.env.template
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plugins/tooluniverse/skills/tooluniverse-gene-enrichment/SKILL.md
- Network accessline 497
gseapy documentation: https://gseapy.readthedocs.io/ - Network accessline 498
PANTHER API: http://pantherdb.org/services/oai/pantherdb/ - Network accessline 499
STRING API: https://string-db.org/cgi/help?sessionId=&subpage=api - Network accessline 500
Reactome Analysis: https://reactome.org/AnalysisService/
plugins/tooluniverse/skills/tooluniverse-gene-enrichment/references/troubleshooting.md
- Network accessline 603
1. **Check gseapy documentation**: https://gseapy.readthedocs.io/
plugins/tooluniverse/skills/tooluniverse-gene-enrichment/scripts/condition_enrichment_screen.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-gene-enrichment/scripts/enrichgo_runner.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-gene-enrichment/scripts/format_enrichment_output.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-gene-enrichment/scripts/gseapy_enrichment_runner.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-glycobiology/references/glygen_tool_reference.md
- Network accessline 25
"species": ["Homo sapiens", "..."], "composition": [ {"name": "N-Acetylhexosamine", "residue": "hexnac", "count": 4, "cid": "...", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/..."}, - Network accessline 26
"composition": [ {"name": "N-Acetylhexosamine", "residue": "hexnac", "count": 4, "cid": "...", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/..."}, {"name": "N-Acetyl-Neuraminic Acid", "residue": "neuac", "count": 2, "cid": - Network accessline 27
{"name": "N-Acetylhexosamine", "residue": "hexnac", "count": 4, "cid": "...", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/..."}, - Network accessline 28
{"name": "N-Acetyl-Neuraminic Acid", "residue": "neuac", "count": 2, "cid": "439197", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/439197"} - Network accessline 159
"glycosylation": ["..."], "snv": ["..."], "species": [{"common_name": "Human", "glygen_name": "Human", "reference_species": "Homo sapiens [9606]", "url": "https://www.uniprot.org/uniprotkb/P14210"}], - Network accessline 160
"snv": ["..."], "species": [{"common_name": "Human", "glygen_name": "Human", "reference_species": "Homo sapiens [9606]", "url": "https://www.uniprot.org/uniprotkb/P14210"}], "all_sites_count": 3, - Network accessline 161
"species": [{"common_name": "Human", "glygen_name": "Human", "reference_species": "Homo sapiens [9606]", "url": "https://www.uniprot.org/uniprotkb/P14210"}],
plugins/tooluniverse/skills/tooluniverse-gpcr-structural-pharmacology/SKILL.md
- Network accessline 293
- GPCRdb: https://gpcrdb.org — standardized GPCR data with generic numbering - Network accessline 294
- SAbDab: https://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab — structural antibody database - Network accessline 295
- PDBePISA: https://www.ebi.ac.uk/pdbe/pisa — protein interface analysis
plugins/tooluniverse/skills/tooluniverse-gwas-drug-discovery/QUICK_START.md
- Network accessline 30
# Or from source git clone https://github.com/example/tooluniverse.git - Network accessline 31
git clone https://github.com/example/tooluniverse.git - Network accessline 121
// Initialize MCP client const client = new MCPClient({ serverUrl: "http://localhost:3000/tooluniverse" - Network accessline 122
const client = new MCPClient({ serverUrl: "http://localhost:3000/tooluniverse" }); - Network accessline 123
serverUrl: "http://localhost:3000/tooluniverse" - Network accessline 1021
- [GWAS Catalog Documentation](https://www.ebi.ac.uk/gwas/docs) - Network accessline 1022
- [Open Targets Platform](https://platform.opentargets.org/) - Network accessline 1023
- [ChEMBL Database](https://www.ebi.ac.uk/chembl/) - Network accessline 1024
- [ToolUniverse Documentation](https://docs.tooluniverse.org)
plugins/tooluniverse/skills/tooluniverse-gwas-drug-discovery/python_implementation.py
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Executable file
plugins/tooluniverse/skills/tooluniverse-gwas-finemapping/.env.template
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plugins/tooluniverse/skills/tooluniverse-gwas-finemapping/SKILL.md
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**Where to get the raw inputs**: these three tools compute on arrays you supply — they do not fetch summary statistics themselves. `src/tooluniverse/data/gwas_sumstats_tools.json`'s 3 tools (`GWASSumStats_list_studies`, `GWASSumStats_get_tr
plugins/tooluniverse/skills/tooluniverse-gwas-finemapping/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-gwas-snp-interpretation/.env.template
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plugins/tooluniverse/skills/tooluniverse-gwas-snp-interpretation/SKILL.md
plugins/tooluniverse/skills/tooluniverse-gwas-snp-interpretation/python_implementation.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-gwas-study-explorer/.env.template
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plugins/tooluniverse/skills/tooluniverse-gwas-study-explorer/python_implementation.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-gwas-trait-to-gene/.env.template
- Access to secretsline 2
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plugins/tooluniverse/skills/tooluniverse-gwas-trait-to-gene/SKILL.md
- Network accessline 224
# Download full GWAS Catalog (all associations, ~37MB TSV) url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 225
url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 231
# Per-study associations via REST study_id = "GCST001234" assocs = requests.get(f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations").json() - Network accessline 233
assocs = requests.get(f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations").json() - Network accessline 274
**GWAS Catalog** - Curator: EBI and NHGRI - URL: https://www.ebi.ac.uk/gwas/ - Network accessline 275
- Curator: EBI and NHGRI - URL: https://www.ebi.ac.uk/gwas/ - Coverage: 100,000+ publications, 500,000+ associations - Network accessline 276
- URL: https://www.ebi.ac.uk/gwas/ - Network accessline 280
**Open Targets Genetics** - Curator: Open Targets consortium - URL: https://genetics.opentargets.org/ - Network accessline 281
- Curator: Open Targets consortium - URL: https://genetics.opentargets.org/ - Coverage: Fine-mapped GWAS, L2G predictions, QTL colocalization - Network accessline 282
- URL: https://genetics.opentargets.org/
plugins/tooluniverse/skills/tooluniverse-gwas-trait-to-gene/python_implementation.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-image-analysis/scripts/batch_process.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-image-analysis/scripts/measure_fluorescence.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-image-analysis/scripts/segment_cells.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-immune-repertoire-analysis/ANALYSIS_DETAILS.md
plugins/tooluniverse/skills/tooluniverse-immunotherapy-response-prediction/SKILL.md
- Network accessline 201
- OpenTargets: https://platform.opentargets.org - Network accessline 202
- CIViC: https://civicdb.org - Network accessline 203
- FDA Drug Labels: https://dailymed.nlm.nih.gov - Network accessline 204
- DrugBank: https://go.drugbank.com - Network accessline 205
- PubMed: https://pubmed.ncbi.nlm.nih.gov - Network accessline 206
- IEDB: https://www.iedb.org - Network accessline 207
- HPA: https://www.proteinatlas.org - Network accessline 208
- cBioPortal: https://www.cbioportal.org
plugins/tooluniverse/skills/tooluniverse-infectious-disease/CHECKLIST.md
- Access to secretsline 33
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-infectious-disease/SKILL.md
plugins/tooluniverse/skills/tooluniverse-infectious-disease/TOOLS_REFERENCE.md
plugins/tooluniverse/skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-install-skills/SKILL.md
- Network accessline 33
# 1. Download skills from GitHub (shallow, sparse — only skills/ folder) git clone --depth 1 --filter=blob:none --sparse \ https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Network accessline 34
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Network accessline 35
https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 49
rm -rf /tmp/tu-skills
plugins/tooluniverse/skills/tooluniverse-lab-data-standardization/references/schema_overview.md
- Network accessline 10
"tier": "native" | "fallback", "note": "<present only for tier=fallback: 'not ASM-validated, best-effort structural parse'>", "manifest": "http://purl.allotrope.org/... (Tier 1 only)", - Network accessline 11
"note": "<present only for tier=fallback: 'not ASM-validated, best-effort structural parse'>", "manifest": "http://purl.allotrope.org/... (Tier 1 only)", "source_file": "plate_reader_run1.csv", - Network accessline 12
"manifest": "http://purl.allotrope.org/... (Tier 1 only)",
plugins/tooluniverse/skills/tooluniverse-lab-data-standardization/scripts/convert_instrument_data.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-lab-data-standardization/scripts/flatten_to_csv.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-lab-data-standardization/scripts/validate_output.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-lab-protocols/SKILL.md
- Access to secretsline 4
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- Search (`ProtocolsIO_search_protocols`) uses the v3 API; per-protocol detail and steps use the newer v4 API — this is a real API-versioning quirk in protocols.io itself, not an inconsistency in this tool. - **Live-verified**: an invalid/m - Network accessline 59
- The response-body schemas here (protocol fields, step structure) were built directly from protocols.io's published API documentation (https://apidoc.protocols.io/), not live-verified end-to-end in this environment (no real API key was ava
plugins/tooluniverse/skills/tooluniverse-literature-deep-research/EXAMPLES.md
- Network accessline 439
"citation_count": 612, "oa_status": "gold", "oa_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704954/" - Network accessline 440
"oa_status": "gold", "oa_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704954/" } - Network accessline 441
"oa_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704954/"
plugins/tooluniverse/skills/tooluniverse-literature-deep-research/FULLTEXT_STRATEGY.md
- Network accessline 84
get_webpage_text_from_url(url="https://doi.org/10.1016/...")
plugins/tooluniverse/skills/tooluniverse-literature-deep-research/REPORT_TEMPLATE.md
plugins/tooluniverse/skills/tooluniverse-literature-deep-research/SKILL.md
- Network accessline 167
**Citation cross-references — naming collision, read carefully**: `src/tooluniverse/data/europepmc_citations_tools.json` defines `EPMC_get_citations`, `EPMC_get_references`, and `EuropePMC_get_article_datalinks` — do NOT confuse these with - Access to secretsline 171
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plugins/tooluniverse/skills/tooluniverse-literature-deep-research/TOOL_NAMES_REFERENCE.md
- Access to secretsline 63
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plugins/tooluniverse/skills/tooluniverse-medical-imaging-radiology/SKILL.md
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what imaging exists — not the images themselves. If the user wants the actual pixels, tell them the DICOM download UI/API is a separate TCIA service (`https://www.cancerimagingarchive.net/`) outside this tool set. - Network accessline 25
service (`https://www.cancerimagingarchive.net/`) outside this tool set.
plugins/tooluniverse/skills/tooluniverse-medical-imaging-radiology/references/tcia_tool_reference.md
- Network accessline 3
All 10 tools live-tested against the real TCIA API (`https://services.cancerimagingarchive.net/nbia-api/`) on 2026-09-17. - Network accessline 4
(`https://services.cancerimagingarchive.net/nbia-api/`) on 2026-09-17. - Network accessline 97
"ImageCount": 2, "FileSize": 16357620, "LicenseName": "Creative Commons Attribution 3.0 Unported License", "LicenseURI": "http://creativecommons.org/licenses/by/3.0/", - Network accessline 98
"LicenseName": "Creative Commons Attribution 3.0 Unported License", "LicenseURI": "http://creativecommons.org/licenses/by/3.0/", "CollectionURI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX"} - Network accessline 99
"LicenseURI": "http://creativecommons.org/licenses/by/3.0/", - Network accessline 100
"CollectionURI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX"} - Network accessline 172
{"Series UID": "1.3.6.1.4.1.14519.5.2.1.6279.6001.141365756818074696859567662357", "Collection": "LIDC-IDRI", "Data Description URI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX", - Network accessline 173
"Collection": "LIDC-IDRI", "Data Description URI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX", "Subject ID": "LIDC-IDRI-0001", - Network accessline 174
"Data Description URI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX",
plugins/tooluniverse/skills/tooluniverse-meta-analysis/scripts/meta_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-metabolomics-analysis/SKILL.md
- Network accessline 206
**Methods**: - MetaboAnalyst: https://doi.org/10.1093/nar/gkab382 - Network accessline 207
- MetaboAnalyst: https://doi.org/10.1093/nar/gkab382 - Network accessline 208
- XCMS: https://doi.org/10.1021/ac051437y - Network accessline 209
- MSEA: https://doi.org/10.1186/1471-2105-11-395 - Network accessline 211
**Databases**: - HMDB: https://hmdb.ca - Network accessline 212
- HMDB: https://hmdb.ca - Network accessline 213
- KEGG Compound: https://www.genome.jp/kegg/compound/ - Network accessline 214
- Reactome: https://reactome.org
plugins/tooluniverse/skills/tooluniverse-metabolomics/.env.template
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[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-metabolomics/QUICK_START.md
- Access to secretsline 415
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[Credential-related source omitted] - Network accessline 503
- **ToolUniverse Documentation**: https://github.com/mims-harvard/ToolUniverse - Network accessline 504
- **HMDB Database**: https://hmdb.ca - Network accessline 505
- **MetaboLights**: https://www.ebi.ac.uk/metabolights/ - Network accessline 506
- **Metabolomics Workbench**: https://www.metabolomicsworkbench.org - Network accessline 507
- **PubChem**: https://pubchem.ncbi.nlm.nih.gov
plugins/tooluniverse/skills/tooluniverse-metabolomics/diabetes_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-metabolomics/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-metabolomics/scripts/metabolism_ref.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-metabolomics/test1_metabolites.md
- Network accessline 13
**Formula**: C6H12O6 **Molecular Weight**: 180.16 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 15
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 20
**Formula**: C3H5O3- **Molecular Weight**: 89.07 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=lactate&searcher=metabolites - Network accessline 22
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=lactate&searcher=metabolites
plugins/tooluniverse/skills/tooluniverse-metabolomics/test2_study.md
plugins/tooluniverse/skills/tooluniverse-metabolomics/test4_comprehensive.md
- Network accessline 15
**Formula**: C6H12O6 **Molecular Weight**: 180.16 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 17
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 22
**Formula**: C3H3O3- **Molecular Weight**: 87.05 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=pyruvate&searcher=metabolites - Network accessline 24
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=pyruvate&searcher=metabolites - Network accessline 30
**Release Date**: N/A **Modified Time**: 2025-09-02T13:34:14.536288 **HTTP URL**: http://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1 - Network accessline 32
**HTTP URL**: http://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1
plugins/tooluniverse/skills/tooluniverse-ml-inference-utility/SKILL.md
- Network accessline 35
1. **Check for `HF_TOKEN` before promising a result.** If it's unset, tell the user plainly that HuggingFace's serverless inference now requires a token, point them to https://huggingface.co/settings/tokens to create a free one - Network accessline 36
user plainly that HuggingFace's serverless inference now requires a token, point them to https://huggingface.co/settings/tokens to create a free one (a `read`-scope token is sufficient), and stop rather than guessing an output. - Network accessline 37
point them to https://huggingface.co/settings/tokens to create a free one - Network accessline 220
Both **require `REPLICATE_API_TOKEN`** (register at https://replicate.com/account/api-tokens) — **live-verified**: with no token - Network accessline 221
https://replicate.com/account/api-tokens) — **live-verified**: with no token
plugins/tooluniverse/skills/tooluniverse-ml-inference-utility/references/hf_inference_tool_reference.md
plugins/tooluniverse/skills/tooluniverse-molecular-cloning/scripts/cloning_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-multi-omics-integration/SKILL.md
plugins/tooluniverse/skills/tooluniverse-multiomic-disease-characterization/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-network-pharmacology/scripts/network_proximity.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 36
STRING_LINKS = "https://stringdb-downloads.org/download/protein.links.v12.0/9606.protein.links.v12.0.txt.gz" - Network accessline 37
STRING_INFO = "https://stringdb-downloads.org/download/protein.info.v12.0/9606.protein.info.v12.0.txt.gz" - Network accessline 45
if not os.path.exists(path) or os.path.getsize(path) == 0: sys.stderr.write(f"Downloading {name} (one-time)...\n") urllib.request.urlretrieve(url, path) - Network accessline 46
sys.stderr.write(f"Downloading {name} (one-time)...\n") urllib.request.urlretrieve(url, path) return path - Network accessline 47
urllib.request.urlretrieve(url, path)
plugins/tooluniverse/skills/tooluniverse-neurosynth-meta-analysis/SKILL.md
- Network accessline 74
- `references/method.md` — the statistical method in more detail, data provenance, and known limitations - Neurosynth: https://neurosynth.org - Network accessline 75
- Neurosynth: https://neurosynth.org - Network accessline 76
- Data source: https://github.com/neurosynth/neurosynth-data - Network accessline 77
- For a full ALE/MKDA meta-analysis pipeline instead of this simplified proxy: NiMARE (https://nimare.readthedocs.io)
plugins/tooluniverse/skills/tooluniverse-neurosynth-meta-analysis/references/method.md
- Network accessline 5
All data comes from the public `neurosynth/neurosynth-data` GitHub repository (https://github.com/neurosynth/neurosynth-data), maintained by the Neurosynth - Network accessline 6
(https://github.com/neurosynth/neurosynth-data), maintained by the Neurosynth - Network accessline 89
if a fetch 404s. - **For anything publication-grade**: use NiMARE (https://nimare.readthedocs.io), which implements real ALE/MKDA with - Network accessline 90
- **For anything publication-grade**: use NiMARE (https://nimare.readthedocs.io), which implements real ALE/MKDA with proper cluster-level family-wise-error correction against this same - Network accessline 91
(https://nimare.readthedocs.io), which implements real ALE/MKDA with
plugins/tooluniverse/skills/tooluniverse-neurosynth-meta-analysis/scripts/_neurosynth_data.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-neurosynth-meta-analysis/scripts/decode_coordinate.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-neurosynth-meta-analysis/scripts/download_data.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 30
RAW_BASE = "https://raw.githubusercontent.com/neurosynth/neurosynth-data/master" - Access to secretsline 41
[Credential-related source omitted] - Network accessline 51
print(f" downloading {url}") try: with urllib.request.urlopen(url, timeout=60) as resp: - Network accessline 52
try: with urllib.request.urlopen(url, timeout=60) as resp: if resp.status != 200: - Network accessline 53
with urllib.request.urlopen(url, timeout=60) as resp: - Network accessline 92
"\nOne or more downloads failed. Do NOT proceed with analysis on " "partial data - re-run this script, or check " "https://github.com/neurosynth/neurosynth-data for the current " - Network accessline 93
"partial data - re-run this script, or check " "https://github.com/neurosynth/neurosynth-data for the current " "file names if the version number has moved on.", - Network accessline 94
"https://github.com/neurosynth/neurosynth-data for the current "
plugins/tooluniverse/skills/tooluniverse-neurosynth-meta-analysis/scripts/search_by_term.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/SKILL.md
- Network accessline 29
3. **Never invent a pipeline version.** nf-core pipelines are versioned and pinned with `-r <version>`. If you are not certain of the current stable release, tell the user to check `https://nf-co.re/<pipeline>/releases` - Network accessline 30
pinned with `-r <version>`. If you are not certain of the current stable release, tell the user to check `https://nf-co.re/<pipeline>/releases` rather than guessing a version number — the version numbers below were - Network accessline 31
release, tell the user to check `https://nf-co.re/<pipeline>/releases` - Network accessline 290
Apache-2.0. This skill orchestrates them but does not vendor or modify pipeline code. Always pin `-r <version>` and verify current releases at https://nf-co.re before a real run — the versions referenced in this skill's - Network accessline 291
pipeline code. Always pin `-r <version>` and verify current releases at https://nf-co.re before a real run — the versions referenced in this skill's reference files may lag behind the latest nf-core release. - Network accessline 292
https://nf-co.re before a real run — the versions referenced in this skill's
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/references/geo_sra_acquisition.md
- Network accessline 74
curl -L -o sample1.fastq.gz "<direct-url>"
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/references/pipelines/atacseq.md
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/references/pipelines/rnaseq.md
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/references/pipelines/sarek.md
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/references/troubleshooting.md
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/scripts/check_environment.py
- Executable scriptline 1
#!/usr/bin/env python3 - Dangerous shell operationline 48
" sudo systemctl start docker\n" - Dangerous shell operationline 50
" sudo usermod -aG docker $USER # then log out and back in\n" - Network accessline 58
return ( "Install Docker Engine (Linux):\n" " curl -fsSL https://get.docker.com | sh\n" - Network accessline 59
"Install Docker Engine (Linux):\n" " curl -fsSL https://get.docker.com | sh\n" " sudo usermod -aG docker $USER # then log out and back in\n" - Dangerous shell operationline 60
" curl -fsSL https://get.docker.com | sh\n" - Network accessline 60
" curl -fsSL https://get.docker.com | sh\n" - Dangerous shell operationline 61
" sudo usermod -aG docker $USER # then log out and back in\n" - Network accessline 61
" sudo usermod -aG docker $USER # then log out and back in\n" "See https://docs.docker.com/engine/install/ for distro-specific steps.\n" "If you cannot get root/sudo (e.g. a locked-down sandbox), Docker cannot " - Network accessline 62
"See https://docs.docker.com/engine/install/ for distro-specific steps.\n" - Dangerous shell operationline 63
"If you cannot get root/sudo (e.g. a locked-down sandbox), Docker cannot " - Network accessline 64
"be installed here — this skill cannot run a real pipeline in that case." ) return "Install Docker Desktop: https://docs.docker.com/get-docker/" - Network accessline 66
return "Install Docker Desktop: https://docs.docker.com/get-docker/" - Network accessline 74
"Nextflow is not installed.", "Install Nextflow:\n" " curl -s https://get.nextflow.io | bash\n" - Network accessline 75
"Install Nextflow:\n" " curl -s https://get.nextflow.io | bash\n" " mkdir -p ~/bin && mv nextflow ~/bin/\n" - Dangerous shell operationline 76
" curl -s https://get.nextflow.io | bash\n" - Network accessline 76
" curl -s https://get.nextflow.io | bash\n" - Dangerous shell operationline 106
" sudo apt-get update && sudo apt-get install -y openjdk-17-jre-headless\n" - Network accessline 106
" sudo apt-get update && sudo apt-get install -y openjdk-17-jre-headless\n" "Other distros: use your package manager to install a JRE >= 11, " "or a user-local install via https://adoptium.net/ if you lack sudo.", - Network accessline 107
"Other distros: use your package manager to install a JRE >= 11, " "or a user-local install via https://adoptium.net/ if you lack sudo.", ) - Network accessline 108
"or a user-local install via https://adoptium.net/ if you lack sudo.",
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/scripts/detect_data_type.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-nfcore-pipelines/scripts/generate_samplesheet.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-nih-funding-landscape/agents/openai.yaml
plugins/tooluniverse/skills/tooluniverse-nih-funding-landscape/references/public-value-cases.md
- Network accessline 326
type-7 transfer `7R01NS121038-02` in FY2022, and continued through `5R01NS121038-05` at UT Southwestern. These are five full project numbers but one distinct core award. `fetch("R01NS121038")` correctly failed because fetch - Network accessline 327
`5R01NS121038-05` at UT Southwestern. These are five full project numbers but one distinct core award. `fetch("R01NS121038")` correctly failed because fetch requires a full project number; either full endpoint record returned a public - Network accessline 328
one distinct core award. `fetch("R01NS121038")` correctly failed because fetch
plugins/tooluniverse/skills/tooluniverse-nih-funding-landscape/references/tool-reference.md
- Network accessline 46
search_grants result.pi_profile_id -> get_pi_profile(profile_id) rank_institutions result.entity_id -> get_institution_profile / mechanism_mix / funding_growth search result.id OR search_grants result.project_num -> fetch(id) - Network accessline 47
rank_institutions result.entity_id -> get_institution_profile / mechanism_mix / funding_growth search result.id OR search_grants result.project_num -> fetch(id) search_grants result.project_num -> citation/fetch of one full record - Network accessline 48
search result.id OR search_grants result.project_num -> fetch(id) - Network accessline 289
than one row for one core in a fiscal year. Count the core once when answering “how many awards,” but do not discard legitimate row-level dollars. Fetch one of the full project numbers; `fetch(core_project_num)` returns an actionable - Network accessline 290
“how many awards,” but do not discard legitimate row-level dollars. Fetch one of the full project numbers; `fetch(core_project_num)` returns an actionable error because a core ID is not a citation record. - Network accessline 291
of the full project numbers; `fetch(core_project_num)` returns an actionable - Network accessline 315
1. Validate the full project-number form. 2. Run `search_grants(project_num=...)`. 3. If zero, run `fetch(id=...)` only as a canonical-record check; preserve its error message. - Network accessline 316
2. Run `search_grants(project_num=...)`. 3. If zero, run `fetch(id=...)` only as a canonical-record check; preserve its error message. 4. Read `source_status` and say “not found in this snapshot/query.” - Network accessline 317
3. If zero, run `fetch(id=...)` only as a canonical-record check; preserve its error message.
plugins/tooluniverse/skills/tooluniverse-nih-funding-landscape/scripts/verify_live_cases.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 769
"exact award fetch returns public URL", exact_fetch["id"] == "1F31DC023096-01" and exact_fetch["url"].startswith("https://opennih.org/projects/"), - Network accessline 770
exact_fetch["id"] == "1F31DC023096-01" and exact_fetch["url"].startswith("https://opennih.org/projects/"), exact_fetch, - Network accessline 771
and exact_fetch["url"].startswith("https://opennih.org/projects/"),
plugins/tooluniverse/skills/tooluniverse-nih-funding-landscape/scripts/verify_live_contract.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-nih-funding-landscape/scripts/verify_output_links.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-noncoding-rna/SKILL.md
- Network accessline 233
# Step 1: Download TargetScan predicted targets (one-time, ~10MB zipped) # URL: https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip - Network accessline 234
# URL: https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip import pandas as pd import zipfile, io, requests - Network accessline 238
url = "https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip" resp = requests.get(url, timeout=60) - Network accessline 239
resp = requests.get(url, timeout=60) - Network accessline 263
# Option 1: Download from miRTarBase bulk export (requires browser download first) # Go to: https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/ - Network accessline 264
# Go to: https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/ - Network accessline 267
# Option 2: Use the GitHub data dump # https://github.com/jorainer/mirtarbase — R package with cached data - Network accessline 268
# https://github.com/jorainer/mirtarbase — R package with cached data
plugins/tooluniverse/skills/tooluniverse-nutrition-food-composition/SKILL.md
plugins/tooluniverse/skills/tooluniverse-organic-chemistry/SKILL.md
- Access to secretsline 101
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-organic-chemistry/scripts/crystal_validator.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-organic-chemistry/scripts/degrees_of_unsaturation.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-organic-chemistry/scripts/molecular_complexity.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-organic-chemistry/scripts/molecular_formula.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-organic-chemistry/scripts/smiles_verifier.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-organic-chemistry/scripts/stereochem_tracker.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-peptide-target-deorphanization/SKILL.md
- Access to secretsline 62
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-peptide-target-deorphanization/references/phases.md
- Access to secretsline 33
[Credential-related source omitted] - Access to secretsline 37
[Credential-related source omitted] - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 242
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 298
[Credential-related source omitted] - Access to secretsline 308
[Credential-related source omitted] - Access to secretsline 344
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-peptide-target-deorphanization/scripts/cofold_screen.py
- Executable scriptline 1
#!/usr/bin/env python3 - Access to secretsline 10
[Credential-related source omitted] - Access to secretsline 19
[Credential-related source omitted] - Access to secretsline 160
[Credential-related source omitted] - Access to secretsline 177
[Credential-related source omitted] - Access to secretsline 178
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-peptide-target-deorphanization/scripts/deorphanize_peptide.py
plugins/tooluniverse/skills/tooluniverse-pharmacogenomics/SKILL.md
plugins/tooluniverse/skills/tooluniverse-pharmacokinetics/scripts/nca_from_csv.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-pharmacovigilance/SKILL.md
- Network accessline 344
- FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers - Network accessline 345
- DailyMed: https://dailymed.nlm.nih.gov - Network accessline 346
- PharmGKB: https://www.pharmgkb.org - Network accessline 347
- ClinicalTrials.gov: https://clinicaltrials.gov - Network accessline 348
- OpenFDA: https://open.fda.gov - Network accessline 349
- KEGG Drug: https://www.genome.jp/kegg/drug
plugins/tooluniverse/skills/tooluniverse-phylogenetics/SKILL.md
- Network accessline 646
`references/sequence_alignment.md`, `references/tree_building.md`, `references/parsimony_analysis.md`, `scripts/tree_statistics.py` - PhyKIT: https://jlsteenwyk.com/PhyKIT/ - Network accessline 647
- PhyKIT: https://jlsteenwyk.com/PhyKIT/ - Network accessline 648
- Biopython Phylo: https://biopython.org/wiki/Phylo - Network accessline 649
- DendroPy: https://dendropy.org/
plugins/tooluniverse/skills/tooluniverse-phylogenetics/references/troubleshooting.md
- Network accessline 570
- `tree_building.md` - Tree construction methods - `parsimony_analysis.md` - Statistical workflows - PhyKIT documentation: https://jlsteenwyk.com/PhyKIT/ - Network accessline 571
- `parsimony_analysis.md` - Statistical workflows - PhyKIT documentation: https://jlsteenwyk.com/PhyKIT/ - Biopython Phylo tutorial: https://biopython.org/wiki/Phylo - Network accessline 572
- PhyKIT documentation: https://jlsteenwyk.com/PhyKIT/ - Network accessline 573
- Biopython Phylo tutorial: https://biopython.org/wiki/Phylo
plugins/tooluniverse/skills/tooluniverse-phylogenetics/scripts/busco_target_orthologs.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-phylogenetics/scripts/format_alignment.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-phylogenetics/scripts/phykit_batch.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-phylogenetics/scripts/scogs_paired_compare.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-phylogenetics/scripts/scogs_phykit_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-phylogenetics/scripts/tree_statistics.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-polygenic-risk-score/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-polygenic-risk-score/check_data.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-polygenic-risk-score/python_implementation.py
plugins/tooluniverse/skills/tooluniverse-population-genetics/scripts/popgen_calculator.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-precision-medicine-stratification/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-precision-oncology/scripts/gdsc_drug_response.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 34
GDSC_URLS = { "GDSC2": "https://cog.sanger.ac.uk/cancerrxgene/GDSC_release8.5/GDSC2_fitted_dose_response_27Oct23.xlsx", - Network accessline 35
"GDSC2": "https://cog.sanger.ac.uk/cancerrxgene/GDSC_release8.5/GDSC2_fitted_dose_response_27Oct23.xlsx", - Network accessline 36
"GDSC1": "https://cog.sanger.ac.uk/cancerrxgene/GDSC_release8.5/GDSC1_fitted_dose_response_27Oct23.xlsx", - Network accessline 53
if not os.path.exists(path) or os.path.getsize(path) == 0: sys.stderr.write(f"Downloading {dataset} (~21 MB, one-time)...\n") urllib.request.urlretrieve(GDSC_URLS[dataset], path) - Network accessline 54
sys.stderr.write(f"Downloading {dataset} (~21 MB, one-time)...\n") urllib.request.urlretrieve(GDSC_URLS[dataset], path) return pd.read_excel(path) - Network accessline 55
urllib.request.urlretrieve(GDSC_URLS[dataset], path)
plugins/tooluniverse/skills/tooluniverse-primer-design/scripts/primer_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-product-safety-surveillance/SKILL.md
- Network accessline 227
- openFDA: https://open.fda.gov - Network accessline 228
- MAUDE (device adverse events): https://www.fda.gov/medical-devices/mandatory-reporting-requirements-manufacturers-importers-and-device-user-facilities - Network accessline 229
- Device recalls / 510(k): https://www.fda.gov/medical-devices - Network accessline 230
- CAERS (food/supplement/cosmetic): https://www.fda.gov/food/compliance-enforcement/cfsan-adverse-event-reporting-system-caers - Network accessline 231
- Drug shortages: https://www.fda.gov/drugs/drug-shortages - Network accessline 232
- Recall classifications: https://www.fda.gov/safety/industry-guidance-recalls
plugins/tooluniverse/skills/tooluniverse-protein-design-campaign/scripts/preflight_submission.py
- Executable scriptline 1
#!/usr/bin/env python3 - Instruction override signalline 31
), "selector_instruction": re.compile( r"ignore previous instructions|system prompt|rank this first|" - Instruction override signalline 32
"selector_instruction": re.compile( r"ignore previous instructions|system prompt|rank this first|" r"select this candidate|you are (?:ChatGPT|Claude)", - Instruction override signalline 33
r"ignore previous instructions|system prompt|rank this first|"
plugins/tooluniverse/skills/tooluniverse-protein-interactions/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-protein-interactions/DOMAIN_ANALYSIS.md
plugins/tooluniverse/skills/tooluniverse-protein-interactions/PHASE2_DISCOVERIES.md
plugins/tooluniverse/skills/tooluniverse-protein-interactions/QUICK_START.md
- Access to secretsline 134
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-protein-interactions/SKILL.md
- Access to secretsline 230
[Credential-related source omitted] - Access to secretsline 230
[Credential-related source omitted] - Access to secretsline 230
[Credential-related source omitted] - Network accessline 235
- STRING: https://string-db.org/ - Network accessline 235
- STRING: https://string-db.org/ - Network accessline 235
- STRING: https://string-db.org/ - Network accessline 236
- BioGRID: https://thebiogrid.org/ (register for free API key) - Network accessline 236
- BioGRID: https://thebiogrid.org/ (register for free API key) - Network accessline 236
- BioGRID: https://thebiogrid.org/ (register for free API key) - Network accessline 237
- SASBDB: https://www.sasbdb.org/ - Network accessline 237
- SASBDB: https://www.sasbdb.org/ - Network accessline 237
- SASBDB: https://www.sasbdb.org/ - Network accessline 238
- Complex Portal: https://www.ebi.ac.uk/complexportal/ - Network accessline 238
- Complex Portal: https://www.ebi.ac.uk/complexportal/ - Network accessline 238
- Complex Portal: https://www.ebi.ac.uk/complexportal/ - Network accessline 239
- ToolUniverse: https://github.com/mims-harvard/ToolUniverse - Network accessline 239
- ToolUniverse: https://github.com/mims-harvard/ToolUniverse - Network accessline 239
- ToolUniverse: https://github.com/mims-harvard/ToolUniverse
plugins/tooluniverse/skills/tooluniverse-protein-interactions/TOOL_DESCRIPTION_OPTIMIZATION.md
- Network accessline 33
"Query protein and genetic interactions from the BioGRID database. **Prerequisites**: Requires BIOGRID_API_KEY environment variable (request at https://webservice.thebiogrid.org/). Returns experimentally - Network accessline 34
[Credential-related source omitted] - Access to secretsline 34
[Credential-related source omitted] - Network accessline 35
(request at https://webservice.thebiogrid.org/). Returns experimentally - Network accessline 94
- `species`: "NCBI taxonomy ID specifying organism. Common values: 9606 (Homo sapiens/human), 10090 (Mus musculus/mouse), 10116 (Rattus norvegicus/rat), 7227 (Drosophila melanogaster). Default 9606 (human). Find taxonomy IDs at https://www. - Network accessline 228
for Interaction Datasets). Returns curated interactions from published studies with evidence methods, PubMed citations, and throughput information. **Prerequisites**: Requires BIOGRID_API_KEY environment variable (free academic API key at h - Access to secretsline 229
[Credential-related source omitted] - Network accessline 230
variable (free academic API key at https://webservice.thebiogrid.org/). - Network accessline 267
ubiquitination, acetylation, methylation, and other covalent protein modifications with site positions, modifying enzymes, and literature evidence. **Prerequisites**: Requires BIOGRID_API_KEY environment variable (request at https://webserv - Access to secretsline 268
[Credential-related source omitted] - Network accessline 269
variable (request at https://webservice.thebiogrid.org/). - Access to secretsline 296
[Credential-related source omitted] - Access to secretsline 326
[Credential-related source omitted] - Network accessline 336
**Parameter Improvements**: - `pubmed_ids`: "List of PubMed IDs (PMIDs) to query. **Note: plural parameter** - pass as list. Example: ['17200106'] (single paper) or ['17200106', '12345678'] (multiple papers). Returns all protein interaction - Network accessline 337
- `pubmed_ids`: "List of PubMed IDs (PMIDs) to query. **Note: plural parameter** - pass as list. Example: ['17200106'] (single paper) or ['17200106', '12345678'] (multiple papers). Returns all protein interactions curated from these publica - Network accessline 389
**Parameter Improvements**: - `sasbdb_id`: "SASBDB entry identifier. Format: 'SASDXXX' where XXX is alphanumeric (e.g., 'SASDAB7', 'SASD1P8'). Find IDs using SASBDB_search_entries or at https://www.sasbdb.org/. Example: 'SASDAB7' (p53 core - Network accessline 390
- `sasbdb_id`: "SASBDB entry identifier. Format: 'SASDXXX' where XXX is alphanumeric (e.g., 'SASDAB7', 'SASD1P8'). Find IDs using SASBDB_search_entries or at https://www.sasbdb.org/. Example: 'SASDAB7' (p53 core domain structure)." - Access to secretsline 439
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-protein-interactions/python_implementation.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-protein-lof-mechanism/SKILL.md
- Network accessline 55
[Credential-related source omitted] - Access to secretsline 55
[Credential-related source omitted] - Network accessline 56
- **esm package with SAE support**: `pip install 'esm @ git+https://github.com/evolutionaryscale/esm@ee891c52'` - Network accessline 256
git clone https://github.com/Kuhlman-Lab/ThermoMPNN.git - Network accessline 276
- [**Tamarind Bio**](https://www.tamarind.bio/tools/thermompnn) — REST API at `app.tamarind.bio/api/`, `x-api-key` auth - Network accessline 277
- [**Neurosnap**](https://neurosnap.ai/service/ThermoMPNN) — credit-based, HTTP API - Network accessline 278
- [**BioLM**](https://biolm.ai/models/thermompnn/) — `POST /api/v3/thermompnn/predict/`, Token auth - Network accessline 279
- [**ProteinIQ**](https://proteiniq.io/app/thermompnn) — web-first - Network accessline 280
- [**Levitate Bio**](https://support.levitate.bio/api/api-thermompnn/) — REST API
plugins/tooluniverse/skills/tooluniverse-protein-modification-analysis/SKILL.md
- Network accessline 98
**Known broken (verified live, not a guess):** `EBIProteins_get_rna_editing` returns HTTP 404 directly from `https://www.ebi.ac.uk/proteins/api/rna_editing/...` for both of its own documented example accessions (P42262, P28335), while the s
plugins/tooluniverse/skills/tooluniverse-protein-sae-variant-interpretation/SKILL.md
- Network accessline 44
[Credential-related source omitted] - Access to secretsline 44
[Credential-related source omitted] - Network accessline 47
pip install 'esm @ git+https://github.com/evolutionaryscale/esm@ee891c52' - Network accessline 51
**License note**: SAE outputs from Forge are governed by the [Cambrian Inference Clickthrough License](https://www.evolutionaryscale.ai/policies/cambrian-inference-clickthrough-license-agreement) — non-commercial / academic research use onl
plugins/tooluniverse/skills/tooluniverse-protein-structure-prediction/SKILL.md
plugins/tooluniverse/skills/tooluniverse-protein-structure-retrieval/examples.md
- Network accessline 59
# download_text_content (returns {"content", "size", "url", ...}). pdb_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 60
pdb_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" ) - Network accessline 61
url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 66
# Also get as mmCIF (modern format) cif_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 67
cif_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.cif" ) - Network accessline 68
url=f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 211
# Get all available formats from RCSB via download_text_content base = f"https://files.rcsb.org/download/{pdb_id}" - Network accessline 212
base = f"https://files.rcsb.org/download/{pdb_id}" - Network accessline 242
for pdb_id in candidates[:3]: structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id.upper()}.pdb" - Network accessline 243
structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id.upper()}.pdb" ) - Network accessline 244
url=f"https://files.rcsb.org/download/{pdb_id.upper()}.pdb"
plugins/tooluniverse/skills/tooluniverse-protein-therapeutic-design/CHECKLIST.md
plugins/tooluniverse/skills/tooluniverse-protein-therapeutic-design/DESIGN_PROCEDURES.md
- Access to secretsline 50
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-protein-therapeutic-design/SKILL.md
- Access to secretsline 86
[Credential-related source omitted] - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 89
[Credential-related source omitted] - Access to secretsline 90
[Credential-related source omitted] - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 97
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 102
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-protein-therapeutic-design/TOOLS_REFERENCE.md
- Access to secretsline 12
[Credential-related source omitted] - Network accessline 158
|------|---------|----------------| | `PDBeSIFTS_get_best_structures` | Find PDB structures for a UniProt accession (best first) | `uniprot_accession` | | `download_text_content` | Download a PDB file (no dedicated PDB-download tool exists) - Network accessline 159
| `PDBeSIFTS_get_best_structures` | Find PDB structures for a UniProt accession (best first) | `uniprot_accession` | | `download_text_content` | Download a PDB file (no dedicated PDB-download tool exists) | `url` (`https://files.rcsb.org/do - Network accessline 160
| `download_text_content` | Download a PDB file (no dedicated PDB-download tool exists) | `url` (`https://files.rcsb.org/download/<PDB_ID>.pdb`) | - Network accessline 169
pdb_id = structures[0]["pdb_id"].upper() structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 170
structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" )["content"] - Network accessline 171
url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 205
# Get the atomic model (PDB) for design structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 206
structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" )["content"] - Network accessline 207
url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Access to secretsline 422
[Credential-related source omitted] - Access to secretsline 430
[Credential-related source omitted] - Access to secretsline 432
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-proteomics-analysis/SKILL.md
- Network accessline 166
**Databases**: [STRING](https://string-db.org), [PhosphoSitePlus](https://www.phosphosite.org), [CORUM](https://mips.helmholtz-muenchen.de/corum)
plugins/tooluniverse/skills/tooluniverse-proteomics-data-retrieval/SKILL.md
plugins/tooluniverse/skills/tooluniverse-publish-tool/SKILL.md
- Network accessline 17
- If you are connected to the hosted ToolUniverse (you have `list_remote_tools`), call it and look for the user's computer ("your computer ...") and the tool. - Otherwise ask the user whether https://connect.aiscientist.tools/remote-servers - Network accessline 18
- Otherwise ask the user whether https://connect.aiscientist.tools/remote-servers shows the computer **online** with the tool listed. - Network accessline 44
1. Open https://connect.aiscientist.tools/my-tools?create=1 (sign in if asked). The **Publish a tool** window opens. - Network accessline 55
- **Public**: the tool should appear in https://connect.aiscientist.tools/discover (Tools view, "Shared by scientists"). If you have `list_remote_tools`, search for it by name and run it once with the same example.
plugins/tooluniverse/skills/tooluniverse-rare-disease-diagnosis/CHECKLIST.md
- Access to secretsline 51
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-rare-disease-diagnosis/DIAGNOSTIC_WORKFLOW.md
- Access to secretsline 493
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-rare-disease-diagnosis/TOOLS_REFERENCE.md
- Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Network accessline 323
[Credential-related source omitted] - Access to secretsline 323
[Credential-related source omitted] - Access to secretsline 804
[Credential-related source omitted] - Access to secretsline 809
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-regulatory-genomics/SKILL.md
plugins/tooluniverse/skills/tooluniverse-regulatory-variant-analysis/SKILL.md
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/convert_rds_to_csv.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 177
print(" macOS: brew install r") print(" Ubuntu: sudo apt-get install r-base") print(" Or visit: https://cran.r-project.org/") - Dangerous shell operationline 178
print(" Ubuntu: sudo apt-get install r-base") - Network accessline 178
print(" Ubuntu: sudo apt-get install r-base") print(" Or visit: https://cran.r-project.org/") sys.exit(1) - Network accessline 179
print(" Or visit: https://cran.r-project.org/")
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/format_deseq2_output.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/gene_length_correlation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/load_count_matrix.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/multi_strain_venn.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/one_way_anova_f.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/pca_variance.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/r_deseq2_wrapper.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-rnaseq-deseq2/scripts/r_edger_limma_wrapper.py
plugins/tooluniverse/skills/tooluniverse-scvi-tools/scripts/_scvi_common.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-scvi-tools/scripts/differential_expression.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-scvi-tools/scripts/prepare_data.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-scvi-tools/scripts/train_model.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-scvi-tools/scripts/transfer_labels.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-scvi-tools/scripts/validate_adata.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-sdk/REFERENCE.md
- Access to secretsline 75
[Credential-related source omitted] - Access to secretsline 114
[Credential-related source omitted] - Access to secretsline 115
[Credential-related source omitted] - Access to secretsline 116
[Credential-related source omitted] - Network accessline 254
client = ToolUniverseClient("http://server:8080") - Access to secretsline 386
[Credential-related source omitted] - Access to secretsline 387
[Credential-related source omitted] - Access to secretsline 388
[Credential-related source omitted] - Access to secretsline 446
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 466
[Credential-related source omitted] - Access to secretsline 467
[Credential-related source omitted] - Access to secretsline 469
[Credential-related source omitted] - Network accessline 497
- **Main Documentation**: https://zitniklab.hms.harvard.edu/ToolUniverse/ - Network accessline 498
- **API Reference**: https://zitniklab.hms.harvard.edu/ToolUniverse/api/modules.html - Network accessline 499
- **Tutorials**: https://zitniklab.hms.harvard.edu/ToolUniverse/guide/scientific_workflows.html - Network accessline 500
- **GitHub Examples**: https://github.com/mims-harvard/ToolUniverse/tree/main/examples - Network accessline 501
- **Community Slack**: https://join.slack.com/t/tooluniversehq/shared_invite/zt-3dic3eoio-5xxoJch7TLNibNQn5_AREQ
plugins/tooluniverse/skills/tooluniverse-sdk/SKILL.md
plugins/tooluniverse/skills/tooluniverse-self-review/references/ret-scored-evaluation.md
plugins/tooluniverse/skills/tooluniverse-sequence-analysis/scripts/amino_acids.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-sequence-analysis/scripts/biology_facts.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 260
url = f"https://rest.uniprot.org/uniprotkb/{accession}.fasta" try: with urllib.request.urlopen(url, timeout=15) as resp: - Network accessline 261
try: with urllib.request.urlopen(url, timeout=15) as resp: fasta = resp.read().decode("utf-8") - Network accessline 262
with urllib.request.urlopen(url, timeout=15) as resp:
plugins/tooluniverse/skills/tooluniverse-sequence-analysis/scripts/translate_dna.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-share-computer/SKILL.md
- Dangerous shell operationline 37
curl -LsSf https://connect.aiscientist.tools/install.sh | sh - Network accessline 37
curl -LsSf https://connect.aiscientist.tools/install.sh | sh - Network accessline 43
powershell -ExecutionPolicy ByPass -c "irm https://connect.aiscientist.tools/install.ps1 | iex" - Network accessline 51
python -m pip install -r https://connect.aiscientist.tools/install-requirements.txt - Network accessline 89
If they already run an MCP server (Streamable HTTP, for example `http://localhost:8080/mcp`), share it unchanged with `--forward` in step 4. Confirm it answers MCP `tools/list` first; a web page or REST API is not MCP, so wrap it as in path - Network accessline 118
Authorize this computer in your browser: https://connect.aiscientist.tools/activate?user_code=ABCD-EFGH - Network accessline 119
https://connect.aiscientist.tools/activate?user_code=ABCD-EFGH - Network accessline 134
# A. own code ~/.local/bin/tu serve share_tool.py --share --detach --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 135
~/.local/bin/tu serve share_tool.py --share --detach --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 136
# B. existing MCP program ~/.local/bin/tu serve --forward "http://localhost:8080" --share --detach --name "Smith Lab Server" --service https://tooluniverse-backend.onrender.com # C. reviewed models - Network accessline 137
~/.local/bin/tu serve --forward "http://localhost:8080" --share --detach --name "Smith Lab Server" --service https://tooluniverse-backend.onrender.com - Network accessline 139
~/.local/bin/tu serve --allow esm,boltz --share --detach --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 150
- If you are connected to the hosted ToolUniverse (you have `list_remote_tools`), call `list_remote_tools` with the computer's name, then `run_remote_tool` once with the same example you tested in step 3. Compare the answer with the local r - Network accessline 151
- Otherwise ask the user to open https://connect.aiscientist.tools/remote-servers and confirm the computer shows as online with the expected tools. - Network accessline 158
~/.local/bin/tuplatform-service install --tool-file "$PWD/share_tool.py" --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 167
- **Specific colleagues use this computer's tools**: on https://connect.aiscientist.tools/remote-servers, the computer's card has **Create share code**. The user copies the code (or **Copy invitation**) and sends it. A colleague pastes it o
plugins/tooluniverse/skills/tooluniverse-single-cell/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-single-cell/scripts/find_markers.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-single-cell/scripts/normalize_data.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-single-cell/scripts/qc_metrics.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-single-cell/scripts/scrna_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-spatial-transcriptomics/SKILL.md
- Network accessline 316
geo_id = "GSE123456" # Check for h5ad or MTX in supplementary files url = f"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={geo_id}&targ=gsm&view=data" - Network accessline 318
url = f"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={geo_id}&targ=gsm&view=data" - Network accessline 323
# HuBMAP data portal # Search at https://portal.hubmapconsortium.org/search then download via globus or direct link - Network accessline 324
# Search at https://portal.hubmapconsortium.org/search then download via globus or direct link - Network accessline 325
# Human Cell Atlas: https://data.humancellatlas.org/ — download h5ad/loom files - Network accessline 345
**Methods**: - Squidpy: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 346
- Squidpy: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 347
- Cell2location: https://doi.org/10.1038/s41587-021-01139-4 - Network accessline 348
- SpatialDE: https://doi.org/10.1038/nmeth.4636 - Network accessline 350
**Platforms**: - 10x Visium: https://www.10xgenomics.com/products/spatial-gene-expression - Network accessline 351
- 10x Visium: https://www.10xgenomics.com/products/spatial-gene-expression - Network accessline 352
- MERFISH: https://doi.org/10.1126/science.aaa6090 - Network accessline 353
- Slide-seq: https://doi.org/10.1126/science.aaw1219
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/SKILL.md
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/expression_anova.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/format_statistical_output.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/logistic_regression_or.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/model_diagnostics.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/power_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/prepare_ae_cohort.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/r_natural_spline_regression.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/sdtm_ordinal_logistic.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/spline_model_compare.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-statistical-modeling/scripts/stat_tests.py
- Executable scriptline 1
Executable file
plugins/tooluniverse/skills/tooluniverse-structural-proteomics/SKILL.md
- Network accessline 311
- PDBePISA: `operation` is internal, not a public parameter - BMRB: `BMRB_get_entries_by_pdb_id` is a BLAST-based sequence match, not a curated 1:1 PDB<->BMRB cross-reference -- a query can return several candidate entries (or none) for a r - Network accessline 312
- BMRB: `BMRB_get_entries_by_pdb_id` is a BLAST-based sequence match, not a curated 1:1 PDB<->BMRB cross-reference -- a query can return several candidate entries (or none) for a real NMR PDB ID; `BMRB_search_chemical_shifts` is slow (15-20 - Network accessline 313
- MobiDB: `MobiDB_get_protein` and `MobiDB_get_consensus` were verified live (repeatedly, with a 90s timeout) to hang indefinitely against their configured endpoint (`https://mobidb.org/api/download`) -- confirmed independently via direct `
plugins/tooluniverse/skills/tooluniverse-structural-variant-analysis/ANALYSIS_PROCEDURES.md
- Network accessline 130
# Note: ToolUniverse has no DECIPHER tool - look up similar patient cases # manually in the DECIPHER web portal (https://www.deciphergenomics.org). - Network accessline 131
# manually in the DECIPHER web portal (https://www.deciphergenomics.org). - Network accessline 216
# Note: no DECIPHER tool exists in ToolUniverse - check DECIPHER cohorts # for these genes manually at https://www.deciphergenomics.org. - Network accessline 217
# for these genes manually at https://www.deciphergenomics.org.
plugins/tooluniverse/skills/tooluniverse-structural-variant-analysis/SKILL.md
- Network accessline 245
- ClinGen Dosage Sensitivity Map: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/
plugins/tooluniverse/skills/tooluniverse-synthetic-biology-parts/SKILL.md
- Network accessline 31
$ tu run SynBioHub_get_part '{"display_id": "BBa_E0040"}' Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely - Network accessline 32
Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely an HTML login or error page). The part may not exist, or SynBioHub may now - Network accessline 33
'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely - Network accessline 42
1. Tell the user plainly: SynBioHub now requires authentication for these reads; this is an upstream policy change, not a bug in the request. 2. Point them to create a free account at https://synbiohub.org, log in, - Network accessline 43
reads; this is an upstream policy change, not a bug in the request. 2. Point them to create a free account at https://synbiohub.org, log in, and retrieve a session token, then set `SYNBIOHUB_API_TOKEN` in the - Network accessline 44
2. Point them to create a free account at https://synbiohub.org, log in, - Access to secretsline 71
[Credential-related source omitted] - Network accessline 72
deliberately hides any tool with unmet `required_api_keys` until the key is present — verified, not a bug); register for a free key at https://developers.addgene.org/ if it's missing. - Network accessline 73
present — verified, not a bug); register for a free key at https://developers.addgene.org/ if it's missing. - Cloning workflow steps (restriction sites, Gibson/Golden Gate assembly - Network accessline 74
https://developers.addgene.org/ if it's missing.
plugins/tooluniverse/skills/tooluniverse-synthetic-biology-parts/references/synbiohub_tool_reference.md
- Network accessline 32
$ tu run SynBioHub_get_part '{"display_id": "BBa_E0040"}' Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, - Network accessline 33
Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely an HTML login or error page). The part may not exist, or - Network accessline 34
'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, - Network accessline 38
$ tu run SynBioHub_get_part '{"part_uri": "https://synbiohub.org/public/igem/BBa_E0040/1"}' - Network accessline 39
Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely an HTML login or error page). The part may not exist, or - Network accessline 40
'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, - Network accessline 84
- **Parameters:** exactly one of `display_id` (string, e.g. `BBa_E0040` for iGEM BioBricks) or `part_uri` (string, full SynBioHub URI, e.g. `https://synbiohub.org/public/igem/BBa_E0040/1` — typically taken from - Network accessline 85
for iGEM BioBricks) or `part_uri` (string, full SynBioHub URI, e.g. `https://synbiohub.org/public/igem/BBa_E0040/1` — typically taken from a search result's `uri` field). Both are individually optional/nullable - Network accessline 86
`https://synbiohub.org/public/igem/BBa_E0040/1` — typically taken from - Access to secretsline 105
[Credential-related source omitted] - Network accessline 109
[Credential-related source omitted] - Access to secretsline 109
[Credential-related source omitted] - Network accessline 110
[Credential-related source omitted] - Access to secretsline 110
[Credential-related source omitted] - Network accessline 111
https://developers.addgene.org/) to use Addgene lookups alongside this
plugins/tooluniverse/skills/tooluniverse-systems-biology/.env.template
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[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
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[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-systems-biology/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-target-research/IMPLEMENTATION.md
- Access to secretsline 370
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-target-research/REFERENCE.md
plugins/tooluniverse/skills/tooluniverse-target-research/SKILL.md
- Access to secretsline 248
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-toxicology/SKILL.md
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[Credential-related source omitted] - Access to secretsline 361
[Credential-related source omitted] - Network accessline 370
- AOPWiki: https://aopwiki.org - Network accessline 371
- FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers - Network accessline 372
- CTD: http://ctdbase.org - Network accessline 373
- DailyMed: https://dailymed.nlm.nih.gov - Network accessline 374
- OpenFDA: https://open.fda.gov - Network accessline 375
- EPA CompTox: https://comptox.epa.gov/dashboard/ (API docs: https://comptox.epa.gov/ctx-api/docs/)
plugins/tooluniverse/skills/tooluniverse-use-shared-computer/SKILL.md
- Network accessline 15
1. Open https://connect.aiscientist.tools/dashboard (sign in if asked). - Network accessline 26
- Call `list_remote_tools` (optionally with words from the task, such as "protein" or the computer's name). It lists tools on the user's own computers, computers they joined, and published tools, each with an id, a description, its inputs a - Network accessline 27
- Published tools can also be browsed at https://connect.aiscientist.tools/discover ("Shared by scientists"). - Network accessline 42
The user can leave a shared computer on https://connect.aiscientist.tools/remote-servers: its card has **Stop using**. The owner can also turn the code off at any time.
plugins/tooluniverse/skills/tooluniverse-vaccine-design/scripts/population_coverage.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-variant-analysis/.env.template
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[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-variant-analysis/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-variant-analysis/scripts/annotate_variants.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-variant-analysis/scripts/coding_variant_filter.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-variant-analysis/scripts/filter_variants.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-variant-analysis/scripts/gatk_haplotypecaller_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-variant-analysis/scripts/parse_vcf.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-variant-analysis/scripts/variant_fraction.py
- Executable scriptline 1
#!/usr/bin/env python3
plugins/tooluniverse/skills/tooluniverse-variant-interpretation/CHECKLIST.md
- Access to secretsline 257
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-variant-interpretation/SKILL.md
- Access to secretsline 93
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[Credential-related source omitted] - Access to secretsline 94
[Credential-related source omitted] - Access to secretsline 98
[Credential-related source omitted] - Access to secretsline 98
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[Credential-related source omitted] - Access to secretsline 161
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-variant-interpretation/TOOLS_REFERENCE.md
- Network accessline 351
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[Credential-related source omitted] - Access to secretsline 499
[Credential-related source omitted] - Access to secretsline 540
[Credential-related source omitted] - Access to secretsline 625
[Credential-related source omitted] - Access to secretsline 626
[Credential-related source omitted] - Access to secretsline 630
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-variant-predictor-dms-validation/SKILL.md
- Network accessline 182
# The official DeepMind release contains per-variant continuous scores # (not just bin assignments). Not currently wrapped by a TU tool — fetch directly: # https://alphafold.ebi.ac.uk/files/AF-<uniprot_id>-F1-aa-substitutions.csv - Network accessline 183
# (not just bin assignments). Not currently wrapped by a TU tool — fetch directly: # https://alphafold.ebi.ac.uk/files/AF-<uniprot_id>-F1-aa-substitutions.csv # or stream-filter https://storage.googleapis.com/dm_alphamissense/AlphaMissens - Network accessline 184
# https://alphafold.ebi.ac.uk/files/AF-<uniprot_id>-F1-aa-substitutions.csv - Network accessline 185
# or stream-filter https://storage.googleapis.com/dm_alphamissense/AlphaMissense_aa_substitutions.tsv.gz - Access to secretsline 206
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse-variant-to-mechanism/SKILL.md
- Access to secretsline 258
[Credential-related source omitted] - Network accessline 350
# Ensembl VEP REST — functional consequence prediction vep = requests.get(f"https://rest.ensembl.org/vep/human/id/{rsid}?content-type=application/json").json() - Network accessline 351
vep = requests.get(f"https://rest.ensembl.org/vep/human/id/{rsid}?content-type=application/json").json() - Network accessline 353
# GTEx eQTL — tissue-specific expression effects eqtl = requests.get(f"https://gtexportal.org/api/v2/association/singleTissueEqtl", - Network accessline 354
eqtl = requests.get(f"https://gtexportal.org/api/v2/association/singleTissueEqtl", - Network accessline 357
# ClinVar bulk (variant_summary.txt.gz, ~200MB, all variants) # Download once, filter locally: # df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") - Network accessline 358
# Download once, filter locally: # df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") # hits = df[df["RS# (dbSNP)"] == int(rsid.replace("rs",""))] - Network accessline 359
# df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t")
plugins/tooluniverse/skills/tooluniverse/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
plugins/tooluniverse/skills/tooluniverse/REFERENCE.md
- Access to secretsline 139
[Credential-related source omitted]
scripts/build-plugin.sh
scripts/release-plugin.sh
- Executable scriptline 1
#!/usr/bin/env bash - Executable scriptline 1
#!/usr/bin/env bash - Executable scriptline 1
#!/usr/bin/env bash - Executable scriptline 1
#!/usr/bin/env bash - Network accessline 222
echo " - Auto-update users will receive it on next Claude Code startup" echo " - GitHub Release zip will be built and attached by the workflow:" echo " https://github.com/mims-harvard/ToolUniverse/actions/workflows/release-plugin.yml" - Network accessline 222
echo " - Auto-update users will receive it on next Claude Code startup" echo " - GitHub Release zip will be built and attached by the workflow:" echo " https://github.com/mims-harvard/ToolUniverse/actions/workflows/release-plugin.yml" - Network accessline 222
echo " - Auto-update users will receive it on next Claude Code startup" echo " - GitHub Release zip will be built and attached by the workflow:" echo " https://github.com/mims-harvard/ToolUniverse/actions/workflows/release-plugin.yml" - Network accessline 222
echo " - Auto-update users will receive it on next Claude Code startup" echo " - GitHub Release zip will be built and attached by the workflow:" echo " https://github.com/mims-harvard/ToolUniverse/actions/workflows/release-plugin.yml" - Network accessline 224
echo " https://github.com/mims-harvard/ToolUniverse/actions/workflows/release-plugin.yml" - Network accessline 224
echo " https://github.com/mims-harvard/ToolUniverse/actions/workflows/release-plugin.yml" - Network accessline 224
echo " https://github.com/mims-harvard/ToolUniverse/actions/workflows/release-plugin.yml" - Network accessline 224
echo " https://github.com/mims-harvard/ToolUniverse/actions/workflows/release-plugin.yml"
scripts/remote_validation/setup_skill_preflight.py
- Executable scriptline 1
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#!/usr/bin/env python3 - Executable scriptline 1
#!/usr/bin/env python3 - Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 38
@property def endpoint(self) -> str: return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Network accessline 40
return f"http://127.0.0.1:{self.port}/mcp" - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 464
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Access to secretsline 465
[Credential-related source omitted] - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 484
r"^python -m (tooluniverse\.remote\.\S+)", text, re.MULTILINE ) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 485
) endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) if module_match is None or endpoint_match is None: - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Network accessline 486
endpoint_match = re.search(r"endpoint is http://127\.0\.0\.1:(\d+)/mcp", text) - Access to secretsline 577
[Credential-related source omitted] - Access to secretsline 577
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scripts/remote_validation/verify_provider.py
scripts/remote_validation/wait_for_memory.py
skills/create-tooluniverse-skill/SKILL.md
- Network accessline 12
> repository.** `devtu-optimize-skills`, `devtu-create-tool` and > `devtu-fix-tool` moved to the **tu-harness** plugin > ([tooluniverse/tooluniverse-harness](https://github.com/tooluniverse/tooluniverse-harness)), - Network accessline 13
> `devtu-fix-tool` moved to the **tu-harness** plugin > ([tooluniverse/tooluniverse-harness](https://github.com/tooluniverse/tooluniverse-harness)), > because they operate on this repository rather than being something a user of - Network accessline 14
> ([tooluniverse/tooluniverse-harness](https://github.com/tooluniverse/tooluniverse-harness)),
skills/create-tooluniverse-skill/assets/skill_template/QUICK_START.md
skills/create-tooluniverse-skill/assets/skill_template/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/create-tooluniverse-skill/assets/skill_template/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/create-tooluniverse-skill/scripts/test_tools_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/create-tooluniverse-skill/test_tools_template.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/devtu-benchmark-harness/scripts/analyze_failures.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/devtu-benchmark-harness/scripts/analyze_results.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/devtu-benchmark-harness/scripts/check_memorization.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/devtu-benchmark-harness/scripts/compute_capsule_checksums.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/devtu-benchmark-harness/scripts/generate_report.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/devtu-benchmark-harness/scripts/grade_answers.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/devtu-benchmark-harness/scripts/run_eval.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/devtu-benchmark-harness/scripts/run_harness_loop.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/devtu-benchmark-harness/scripts/run_one_by_one.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/devtu-benchmark-harness/scripts/skill_routing_test.py
- Executable scriptline 1
Executable file
skills/devtu-benchmark-harness/scripts/skill_routing_test_v2.py
- Executable scriptline 1
Executable file
skills/devtu-benchmark-harness/scripts/solve_one.sh
- Executable scriptline 1
#!/usr/bin/env bash
skills/host-and-share-remote-tool/SKILL.md
- Network accessline 20
Open ToolUniverse Connect, go to **Computers → Connect a computer** (https://connect.aiscientist.tools/remote-servers), choose the Python or existing-MCP path, and copy the immutable install command shown there. Run it in a new Python 3.12 - Network accessline 59
Start the Streamable HTTP MCP server on loopback, for example `http://127.0.0.1:8080/mcp`. Confirm it implements MCP initialization, `tools/list`, and `tools/call`; a health endpoint alone is insufficient. - Network accessline 78
async def main(): async with Client("http://127.0.0.1:8080/mcp") as client: - Network accessline 79
async with Client("http://127.0.0.1:8080/mcp") as client: - Network accessline 98
tu serve my_tool.py --share --name "My Model" --service https://tooluniverse-backend.onrender.com - Network accessline 104
tuplatform-relay --forward http://127.0.0.1:8080 --name "My Server" --service https://tooluniverse-backend.onrender.com
skills/setup-af2ig-remote-tool/SKILL.md
- Network accessline 239
The Streamable HTTP endpoint is http://127.0.0.1:8053/mcp. In a second activated shell run: - Network accessline 246
async def main(): async with Client("http://127.0.0.1:8053/mcp") as client: - Network accessline 247
async with Client("http://127.0.0.1:8053/mcp") as client: - Network accessline 304
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 305
tu doctor --forward http://127.0.0.1:8053/mcp --json - Network accessline 306
tu serve --share --forward http://127.0.0.1:8053/mcp --name validation-af2ig --workers 1 - Network accessline 350
async def main(): arguments = json.load(open("/tmp/af2ig-example.json")) async with Client("http://127.0.0.1:8053/mcp") as client: - Network accessline 351
arguments = json.load(open("/tmp/af2ig-example.json")) async with Client("http://127.0.0.1:8053/mcp") as client: result = await client.call_tool("af2ig_predict", arguments) - Network accessline 352
async with Client("http://127.0.0.1:8053/mcp") as client: - Network accessline 367
async def main(): pdb = open("src/tooluniverse/remote/af2ig/smoke/input.pdb").read() async with Client("http://127.0.0.1:8053/mcp") as client: - Network accessline 368
pdb = open("src/tooluniverse/remote/af2ig/smoke/input.pdb").read() async with Client("http://127.0.0.1:8053/mcp") as client: result = await client.call_tool( - Network accessline 369
async with Client("http://127.0.0.1:8053/mcp") as client:
skills/setup-af3_jax-remote-tool/SKILL.md
- Network accessline 183
The Streamable HTTP endpoint is http://127.0.0.1:8045/mcp. In a second activated shell run: - Network accessline 190
async def main(): async with Client("http://127.0.0.1:8045/mcp") as client: - Network accessline 191
async with Client("http://127.0.0.1:8045/mcp") as client: - Network accessline 213
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 214
tu doctor --forward http://127.0.0.1:8045/mcp --json - Network accessline 215
tu serve --share --forward http://127.0.0.1:8045/mcp --name validation-af3_jax --workers 1 - Network accessline 247
async def main(): arguments = json.loads(r'''{"sequences":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}],"seed":1}''') async with Client("http://127.0.0.1:8045/mcp") as client: - Network accessline 248
arguments = json.loads(r'''{"sequences":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}],"seed":1}''') async with Client("http://127.0.0.1:8045/mcp") as client: result = await client.call_tool("af3_ - Network accessline 249
async with Client("http://127.0.0.1:8045/mcp") as client:
skills/setup-af3_torch-remote-tool/SKILL.md
- Network accessline 124
The Streamable HTTP endpoint is http://127.0.0.1:8036/mcp. In a second activated shell run: - Network accessline 131
async def main(): async with Client("http://127.0.0.1:8036/mcp") as client: - Network accessline 132
async with Client("http://127.0.0.1:8036/mcp") as client: - Network accessline 150
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 151
tu doctor --forward http://127.0.0.1:8036/mcp --json - Network accessline 152
tu serve --share --forward http://127.0.0.1:8036/mcp --name validation-af3_torch --workers 1 - Network accessline 184
async def main(): arguments = json.loads(r'''{"fold_input": "{\"name\": \"smoke\", \"dialect\": \"alphafold3\", \"version\": 2, \"sequences\": [{\"protein\": {\"id\": \"A\", \"sequence\": \"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF\", \"modif - Network accessline 185
arguments = json.loads(r'''{"fold_input": "{\"name\": \"smoke\", \"dialect\": \"alphafold3\", \"version\": 2, \"sequences\": [{\"protein\": {\"id\": \"A\", \"sequence\": \"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF\", \"modifications\": [], \"temp - Network accessline 186
async with Client("http://127.0.0.1:8036/mcp") as client:
skills/setup-apnovo-remote-tool/SKILL.md
- Network accessline 116
uv venv --python 3.11 ~/ligandmpnn-venv git clone https://github.com/dauparas/LigandMPNN.git ~/LigandMPNN - Network accessline 117
git clone https://github.com/dauparas/LigandMPNN.git ~/LigandMPNN - Network accessline 314
The Streamable HTTP endpoint is http://127.0.0.1:8059/mcp. In a second activated shell run: - Network accessline 321
async def main(): async with Client("http://127.0.0.1:8059/mcp") as client: - Network accessline 322
async with Client("http://127.0.0.1:8059/mcp") as client: - Network accessline 353
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 354
tu doctor --forward http://127.0.0.1:8059/mcp --json - Network accessline 355
tu serve --share --forward http://127.0.0.1:8059/mcp --name validation-apnovo --workers 1 - Network accessline 422
async def main(): arguments = json.loads(r'''{"manifest": "{\"defaults\": {\"num_designs\": 1, \"seed_start\": 0, \"num_sampling_steps\": 1000}, \"designs\": [{\"name\": \"kemp_eliminase\", \"output_prefix\": \"kemp\", \"input_file\": \ - Network accessline 423
arguments = json.loads(r'''{"manifest": "{\"defaults\": {\"num_designs\": 1, \"seed_start\": 0, \"num_sampling_steps\": 1000}, \"designs\": [{\"name\": \"kemp_eliminase\", \"output_prefix\": \"kemp\", \"input_file\": \"motif.cif\", \"motif_ - Network accessline 424
async with Client("http://127.0.0.1:8059/mcp") as client:
skills/setup-atlasfold-remote-tool/SKILL.md
- Network accessline 122
The Streamable HTTP endpoint is http://127.0.0.1:8043/mcp. In a second activated shell run: - Network accessline 129
async def main(): async with Client("http://127.0.0.1:8043/mcp") as client: - Network accessline 130
async with Client("http://127.0.0.1:8043/mcp") as client: - Network accessline 148
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 149
tu doctor --forward http://127.0.0.1:8043/mcp --json - Network accessline 150
tu serve --share --forward http://127.0.0.1:8043/mcp --name validation-atlasfold --workers 1 - Network accessline 182
async def main(): arguments = json.loads(r'''{"chains":["LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"]}''') async with Client("http://127.0.0.1:8043/mcp") as client: - Network accessline 183
arguments = json.loads(r'''{"chains":["LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"]}''') async with Client("http://127.0.0.1:8043/mcp") as client: result = await client.call_tool("atlasfold_predict", arguments) - Network accessline 184
async with Client("http://127.0.0.1:8043/mcp") as client:
skills/setup-bioemu2-remote-tool/SKILL.md
- Network accessline 150
The Streamable HTTP endpoint is http://127.0.0.1:8063/mcp. In a second activated shell run: - Network accessline 157
async def main(): async with Client("http://127.0.0.1:8063/mcp") as client: - Network accessline 158
async with Client("http://127.0.0.1:8063/mcp") as client: - Network accessline 179
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 180
tu doctor --forward http://127.0.0.1:8063/mcp --json - Network accessline 181
tu serve --share --forward http://127.0.0.1:8063/mcp --name validation-bioemu2 --workers 1 - Network accessline 224
async def main(): async with Client("http://127.0.0.1:8063/mcp") as client: - Network accessline 225
async with Client("http://127.0.0.1:8063/mcp") as client:
skills/setup-boltz-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/boltz > curl -fsSL -o ~/.tooluniverse/envs/boltz-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/boltz > curl -fsSL -o ~/.tooluniverse/envs/boltz-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > ~/.local/bin/uv pip install --python ~/.tooluniverse/en - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/boltz-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 14
> # then this model's own dependencies, as in "Create an isolated environment" below > ~/.local/bin/tu remote check boltz --python ~/.tooluniverse/envs/boltz/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow boltz - Network accessline 15
> ~/.local/bin/tu remote check boltz --python ~/.tooluniverse/envs/boltz/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow boltz --python ~/.tooluniverse/envs/boltz/bin/python --share --detach --name "<computer nam - Network accessline 16
> ~/.local/bin/tu serve --allow boltz --python ~/.tooluniverse/envs/boltz/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8080/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8080/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8080/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8080/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8080/mcp --name validation-boltz --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"sequence":"ACDEFGHIKLMNPQRSTVWY","ligands":[{"id":"L1","smiles":"CCO"}],"sampling_steps":20,"recycling_steps":1,"use_msa_server":false}''') async with Client("http://127.0.0.1:8080/mcp") - Network accessline 162
arguments = json.loads('''{"sequence":"ACDEFGHIKLMNPQRSTVWY","ligands":[{"id":"L1","smiles":"CCO"}],"sampling_steps":20,"recycling_steps":1,"use_msa_server":false}''') async with Client("http://127.0.0.1:8080/mcp") as client: re - Network accessline 163
async with Client("http://127.0.0.1:8080/mcp") as client:
skills/setup-boltzgen-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8035/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8035/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8035/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8035/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8035/mcp --name validation-boltzgen --workers 1 - Network accessline 183
async def main(): arguments = json.loads(r'''{"spec": "entities:\n - protein:\n id: A\n sequence: 60..60\n", "num_designs": 2, "seed": 0}''') async with Client("http://127.0.0.1:8035/mcp") as client: - Network accessline 184
arguments = json.loads(r'''{"spec": "entities:\n - protein:\n id: A\n sequence: 60..60\n", "num_designs": 2, "seed": 0}''') async with Client("http://127.0.0.1:8035/mcp") as client: result = await client.call_tool("bo - Network accessline 185
async with Client("http://127.0.0.1:8035/mcp") as client:
skills/setup-borzoi-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/borzoi > curl -fsSL -o ~/.tooluniverse/envs/borzoi-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/borzoi > curl -fsSL -o ~/.tooluniverse/envs/borzoi-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/borzoi-requirement - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/borzoi-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/borzoi-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/borzoi/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/borzoi/bin/python -r ~/.tooluniverse/envs/borzoi-tooluniverse.txt -r ~/.tooluniverse/envs/borzoi-requirements.txt > ~/.local/bin/tu remote check borzoi --python ~/.tooluniverse/env - Network accessline 15
> ~/.local/bin/tu remote check borzoi --python ~/.tooluniverse/envs/borzoi/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow borzoi --python ~/.tooluniverse/envs/borzoi/bin/python --share --detach --name "<computer - Network accessline 16
> ~/.local/bin/tu serve --allow borzoi --python ~/.tooluniverse/envs/borzoi/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8012/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8012/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8012/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8012/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8012/mcp --name validation-borzoi --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"sequence":"ACGTACGT","top_n":1}''') async with Client("http://127.0.0.1:8012/mcp") as client: - Network accessline 162
arguments = json.loads('''{"sequence":"ACGTACGT","top_n":1}''') async with Client("http://127.0.0.1:8012/mcp") as client: result = await client.call_tool("run_borzoi_predict", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8012/mcp") as client:
skills/setup-borzoi_calico-remote-tool/SKILL.md
- Network accessline 130
python -m tooluniverse.remote.borzoi_calico.borzoi_calico_tool tu doctor --forward http://127.0.0.1:8061/mcp --json - Network accessline 131
tu doctor --forward http://127.0.0.1:8061/mcp --json - Network accessline 231
tu doctor --forward http://127.0.0.1:8061/mcp --json - Network accessline 232
tu serve --share --forward http://127.0.0.1:8061/mcp --name validation-borzoi-calico --workers 1 - Network accessline 237
The Streamable HTTP endpoint is http://127.0.0.1:8061/mcp. In a second - Network accessline 254
async def main(): arguments = json.loads('''{"variants":[{"chrom":"chr1","pos":43110773,"ref":"G","alt":"A"}],"top_tracks":5}''') async with Client("http://127.0.0.1:8061/mcp") as client: - Network accessline 255
arguments = json.loads('''{"variants":[{"chrom":"chr1","pos":43110773,"ref":"G","alt":"A"}],"top_tracks":5}''') async with Client("http://127.0.0.1:8061/mcp") as client: result = await client.call_tool("run_borzoi_calico_sad", a - Network accessline 256
async with Client("http://127.0.0.1:8061/mcp") as client:
skills/setup-cell2location-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/cell2location > curl -fsSL -o ~/.tooluniverse/envs/cell2location-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/cell2location > curl -fsSL -o ~/.tooluniverse/envs/cell2location-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/cell - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/cell2location-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/cell2location-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/cell2location/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/cell2location/bin/python -r ~/.tooluniverse/envs/cell2location-tooluniverse.txt -r ~/.tooluniverse/envs/cell2location-requirements.txt > ~/.local/bin/tu remote check cell2location - Network accessline 15
> ~/.local/bin/tu remote check cell2location --python ~/.tooluniverse/envs/cell2location/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow cell2location --python ~/.tooluniverse/envs/cell2location/bin/python --shar - Network accessline 16
> ~/.local/bin/tu serve --allow cell2location --python ~/.tooluniverse/envs/cell2location/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8019/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8019/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8019/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8019/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8019/mcp --name validation-cell2location --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"sc_path":"reference.h5ad","sp_path":"spatial.h5ad","cluster_label":"cell_type","ref_epochs":1,"sp_epochs":1}''') async with Client("http://127.0.0.1:8019/mcp") as client: - Network accessline 162
arguments = json.loads('''{"sc_path":"reference.h5ad","sp_path":"spatial.h5ad","cluster_label":"cell_type","ref_epochs":1,"sp_epochs":1}''') async with Client("http://127.0.0.1:8019/mcp") as client: result = await client.call_to - Network accessline 163
async with Client("http://127.0.0.1:8019/mcp") as client:
skills/setup-cellrank-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/cellrank > curl -fsSL -o ~/.tooluniverse/envs/cellrank-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/cellrank > curl -fsSL -o ~/.tooluniverse/envs/cellrank-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/cellrank-requi - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/cellrank-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/cellrank-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/cellrank/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/cellrank/bin/python -r ~/.tooluniverse/envs/cellrank-tooluniverse.txt -r ~/.tooluniverse/envs/cellrank-requirements.txt > ~/.local/bin/tu remote check cellrank --python ~/.tooluniv - Network accessline 15
> ~/.local/bin/tu remote check cellrank --python ~/.tooluniverse/envs/cellrank/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow cellrank --python ~/.tooluniverse/envs/cellrank/bin/python --share --detach --name "< - Network accessline 16
> ~/.local/bin/tu serve --allow cellrank --python ~/.tooluniverse/envs/cellrank/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8028/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8028/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8028/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8028/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8028/mcp --name validation-cellrank --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","kernel":"connectivity","n_states":2}''') async with Client("http://127.0.0.1:8028/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","kernel":"connectivity","n_states":2}''') async with Client("http://127.0.0.1:8028/mcp") as client: result = await client.call_tool("run_cellrank_fate", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8028/mcp") as client:
skills/setup-celltypist-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/celltypist > curl -fsSL -o ~/.tooluniverse/envs/celltypist-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/celltypist > curl -fsSL -o ~/.tooluniverse/envs/celltypist-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/celltypist - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/celltypist-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/celltypist-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/celltypist/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/celltypist/bin/python -r ~/.tooluniverse/envs/celltypist-tooluniverse.txt -r ~/.tooluniverse/envs/celltypist-requirements.txt > ~/.local/bin/tu remote check celltypist --python ~/. - Network accessline 15
> ~/.local/bin/tu remote check celltypist --python ~/.tooluniverse/envs/celltypist/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow celltypist --python ~/.tooluniverse/envs/celltypist/bin/python --share --detach - - Network accessline 16
> ~/.local/bin/tu serve --allow celltypist --python ~/.tooluniverse/envs/celltypist/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 131
The Streamable HTTP endpoint is http://127.0.0.1:8014/mcp. In a second activated shell run: - Network accessline 138
async def main(): async with Client("http://127.0.0.1:8014/mcp") as client: - Network accessline 139
async with Client("http://127.0.0.1:8014/mcp") as client: - Network accessline 153
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 154
tu doctor --forward http://127.0.0.1:8014/mcp --json - Network accessline 155
tu serve --share --forward http://127.0.0.1:8014/mcp --name validation-celltypist --workers 1 - Network accessline 182
async def main(): arguments = json.loads('''{"adata_path":"celltypist_official_sample_80.h5ad","model":"Immune_All_Low.pkl","majority_voting":true}''') async with Client("http://127.0.0.1:8014/mcp") as client: - Network accessline 183
arguments = json.loads('''{"adata_path":"celltypist_official_sample_80.h5ad","model":"Immune_All_Low.pkl","majority_voting":true}''') async with Client("http://127.0.0.1:8014/mcp") as client: result = await client.call_tool("run - Network accessline 184
async with Client("http://127.0.0.1:8014/mcp") as client:
skills/setup-chai1-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8033/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8033/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8033/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8033/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8033/mcp --name validation-chai1 --workers 1 - Network accessline 183
async def main(): arguments = json.loads(r'''{"fasta":">protein|name=A\nMKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQ\n","seed":0,"num_diffn_samples":1}''') async with Client("http://127.0.0.1:8033/mcp") as client: - Network accessline 184
arguments = json.loads(r'''{"fasta":">protein|name=A\nMKTAYIAKQRQISFVKSHFSRQLEERLGLIEVQ\n","seed":0,"num_diffn_samples":1}''') async with Client("http://127.0.0.1:8033/mcp") as client: result = await client.call_tool("chai1_fold - Network accessline 185
async with Client("http://127.0.0.1:8033/mcp") as client:
skills/setup-chrombpnet-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/chrombpnet > curl -fsSL -o ~/.tooluniverse/envs/chrombpnet-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/chrombpnet > curl -fsSL -o ~/.tooluniverse/envs/chrombpnet-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/chrombpnet - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/chrombpnet-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/chrombpnet-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/chrombpnet/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/chrombpnet/bin/python -r ~/.tooluniverse/envs/chrombpnet-tooluniverse.txt -r ~/.tooluniverse/envs/chrombpnet-requirements.txt > ~/.local/bin/tu remote check chrombpnet --python ~/. - Network accessline 15
> ~/.local/bin/tu remote check chrombpnet --python ~/.tooluniverse/envs/chrombpnet/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow chrombpnet --python ~/.tooluniverse/envs/chrombpnet/bin/python --share --detach - - Network accessline 16
> ~/.local/bin/tu serve --allow chrombpnet --python ~/.tooluniverse/envs/chrombpnet/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8032/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8032/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8032/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8032/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8032/mcp --name validation-chrombpnet --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"sequence":"ACGTACGT"}''') async with Client("http://127.0.0.1:8032/mcp") as client: - Network accessline 162
arguments = json.loads('''{"sequence":"ACGTACGT"}''') async with Client("http://127.0.0.1:8032/mcp") as client: result = await client.call_tool("run_chrombpnet_predict", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8032/mcp") as client:
skills/setup-colabdesign-remote-tool/SKILL.md
- Network accessline 112
- No credentials. `COLABDESIGN_PARAMS_DIR` (default `~/.colabdesign`) holds 1.7 GiB in five declared entries, `params/params_model_{1..5}_multimer_v3.npz`, each with a size and a digest. They are DeepMind's AlphaFold parameters (CC BY 4.0), - Network accessline 165
The Streamable HTTP endpoint is http://127.0.0.1:8054/mcp. In a second activated shell run: - Network accessline 172
async def main(): async with Client("http://127.0.0.1:8054/mcp") as client: - Network accessline 173
async with Client("http://127.0.0.1:8054/mcp") as client: - Network accessline 187
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 188
tu doctor --forward http://127.0.0.1:8054/mcp --json - Network accessline 189
tu serve --share --forward http://127.0.0.1:8054/mcp --name validation-colabdesign --workers 1 - Network accessline 230
async def main(): async with Client("http://127.0.0.1:8054/mcp") as client: - Network accessline 231
async with Client("http://127.0.0.1:8054/mcp") as client:
skills/setup-colabfold-remote-tool/SKILL.md
- Network accessline 150
The Streamable HTTP endpoint is http://127.0.0.1:8051/mcp. In a second activated shell run: - Network accessline 157
async def main(): async with Client("http://127.0.0.1:8051/mcp") as client: - Network accessline 158
async with Client("http://127.0.0.1:8051/mcp") as client: - Network accessline 178
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 179
tu doctor --forward http://127.0.0.1:8051/mcp --json - Network accessline 180
tu serve --share --forward http://127.0.0.1:8051/mcp --name validation-colabfold --workers 1 - Network accessline 220
async def main(): async with Client("http://127.0.0.1:8051/mcp") as client: - Network accessline 221
async with Client("http://127.0.0.1:8051/mcp") as client:
skills/setup-complexa-remote-tool/SKILL.md
- Network accessline 145
The Streamable HTTP endpoint is http://127.0.0.1:8056/mcp. In a second activated shell run: - Network accessline 152
async def main(): async with Client("http://127.0.0.1:8056/mcp") as client: - Network accessline 153
async with Client("http://127.0.0.1:8056/mcp") as client: - Network accessline 169
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 170
tu doctor --forward http://127.0.0.1:8056/mcp --json - Network accessline 171
tu serve --share --forward http://127.0.0.1:8056/mcp --name validation-complexa --workers 1 - Network accessline 211
async def main(): async with Client("http://127.0.0.1:8056/mcp", timeout=3600) as client: - Network accessline 212
async with Client("http://127.0.0.1:8056/mcp", timeout=3600) as client:
skills/setup-depmap-24q2-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/depmap-24q2 > curl -fsSL -o ~/.tooluniverse/envs/depmap-24q2-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/depmap-24q2 > curl -fsSL -o ~/.tooluniverse/envs/depmap-24q2-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/depmap-2 - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/depmap-24q2-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/depmap-24q2-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/depmap_24q2/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/depmap-24q2/bin/python -r ~/.tooluniverse/envs/depmap-24q2-tooluniverse.txt -r ~/.tooluniverse/envs/depmap-24q2-requirements.txt > ~/.local/bin/tu remote check depmap-24q2 --python - Network accessline 15
> ~/.local/bin/tu remote check depmap-24q2 --python ~/.tooluniverse/envs/depmap-24q2/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow depmap-24q2 --python ~/.tooluniverse/envs/depmap-24q2/bin/python --share --deta - Network accessline 16
> ~/.local/bin/tu serve --allow depmap-24q2 --python ~/.tooluniverse/envs/depmap-24q2/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:7002/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:7002/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:7002/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:7002/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:7002/mcp --name validation-depmap-24q2 --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"gene_a":"BRAF","gene_b":"MAPK1"}''') async with Client("http://127.0.0.1:7002/mcp") as client: - Network accessline 162
arguments = json.loads('''{"gene_a":"BRAF","gene_b":"MAPK1"}''') async with Client("http://127.0.0.1:7002/mcp") as client: result = await client.call_tool("compute_depmap24q2_gene_correlations", arguments) - Network accessline 163
async with Client("http://127.0.0.1:7002/mcp") as client:
skills/setup-e1-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8044/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8044/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8044/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8044/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8044/mcp --name validation-e1 --workers 1 - Network accessline 183
async def main(): arguments = json.loads(r'''{"parent_sequence": "FWDKESRSPHESAPQYARKIWEMAAAVAPHQATIRSVINIIRLAQVEGLEMTQTHLLWSTPWCSIPQGNVND", "mutant_sequences": ">S6R\nFWDKERRSPHESAPQYARKIWEMAAAVAPHQATIRSVINIIRLAQVEGLEMTQTHLLWSTPWCSIPQG - Network accessline 184
arguments = json.loads(r'''{"parent_sequence": "FWDKESRSPHESAPQYARKIWEMAAAVAPHQATIRSVINIIRLAQVEGLEMTQTHLLWSTPWCSIPQGNVND", "mutant_sequences": ">S6R\nFWDKERRSPHESAPQYARKIWEMAAAVAPHQATIRSVINIIRLAQVEGLEMTQTHLLWSTPWCSIPQGNVND\n>R18V\nFWDKESRSP - Network accessline 185
async with Client("http://127.0.0.1:8044/mcp") as client:
skills/setup-ef2inv-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8042/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8042/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8042/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8042/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8042/mcp --name validation-ef2inv --workers 1 - Network accessline 183
async def main(): arguments = json.loads(r'''{"target_name": "pd-l1", "binder_len": 60, "n_designs": 1, "seed": 0}''') async with Client("http://127.0.0.1:8042/mcp") as client: - Network accessline 184
arguments = json.loads(r'''{"target_name": "pd-l1", "binder_len": 60, "n_designs": 1, "seed": 0}''') async with Client("http://127.0.0.1:8042/mcp") as client: result = await client.call_tool("ef2inv_design", arguments) - Network accessline 185
async with Client("http://127.0.0.1:8042/mcp") as client:
skills/setup-enformer-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/enformer > curl -fsSL -o ~/.tooluniverse/envs/enformer-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/enformer > curl -fsSL -o ~/.tooluniverse/envs/enformer-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/enformer-requi - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/enformer-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/enformer-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/enformer/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/enformer/bin/python -r ~/.tooluniverse/envs/enformer-tooluniverse.txt -r ~/.tooluniverse/envs/enformer-requirements.txt > ~/.local/bin/tu remote check enformer --python ~/.tooluniv - Network accessline 15
> ~/.local/bin/tu remote check enformer --python ~/.tooluniverse/envs/enformer/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow enformer --python ~/.tooluniverse/envs/enformer/bin/python --share --detach --name "< - Network accessline 16
> ~/.local/bin/tu serve --allow enformer --python ~/.tooluniverse/envs/enformer/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8011/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8011/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8011/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8011/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8011/mcp --name validation-enformer --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"sequence":"ACGTACGT","organism":"human","top_n":1}''') async with Client("http://127.0.0.1:8011/mcp") as client: - Network accessline 162
arguments = json.loads('''{"sequence":"ACGTACGT","organism":"human","top_n":1}''') async with Client("http://127.0.0.1:8011/mcp") as client: result = await client.call_tool("run_enformer_predict", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8011/mcp") as client:
skills/setup-enformer_deepmind-remote-tool/SKILL.md
- Network accessline 126
tu doctor --forward http://127.0.0.1:8060/mcp --json - Network accessline 209
tu doctor --forward http://127.0.0.1:8060/mcp --json - Network accessline 210
tu serve --share --forward http://127.0.0.1:8060/mcp --name validation-enformer-deepmind --workers 1 - Network accessline 215
The Streamable HTTP endpoint is http://127.0.0.1:8060/mcp. In a second - Network accessline 247
if smoke.is_file(): arguments["sequence"] = smoke.read_text().strip() async with Client("http://127.0.0.1:8060/mcp") as client: - Network accessline 248
arguments["sequence"] = smoke.read_text().strip() async with Client("http://127.0.0.1:8060/mcp") as client: result = await client.call_tool("run_enformer_deepmind_predict", arguments) - Network accessline 249
async with Client("http://127.0.0.1:8060/mcp") as client:
skills/setup-esm-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/esm > curl -fsSL -o ~/.tooluniverse/envs/esm-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/esm > curl -fsSL -o ~/.tooluniverse/envs/esm-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/esm-requirements.txt htt - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/esm-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/esm-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/esm/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/esm/bin/python -r ~/.tooluniverse/envs/esm-tooluniverse.txt -r ~/.tooluniverse/envs/esm-requirements.txt > ~/.local/bin/tu remote check esm --python ~/.tooluniverse/envs/esm/bin/py - Network accessline 15
> ~/.local/bin/tu remote check esm --python ~/.tooluniverse/envs/esm/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow esm --python ~/.tooluniverse/envs/esm/bin/python --share --detach --name "<computer name>" --se - Network accessline 16
> ~/.local/bin/tu serve --allow esm --python ~/.tooluniverse/envs/esm/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8008/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8008/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8008/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8008/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8008/mcp --name validation-esm --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"sequence":"MKT"}''') async with Client("http://127.0.0.1:8008/mcp") as client: - Network accessline 162
arguments = json.loads('''{"sequence":"MKT"}''') async with Client("http://127.0.0.1:8008/mcp") as client: result = await client.call_tool("esm_embed_sequence", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8008/mcp") as client:
skills/setup-esm_if1-remote-tool/SKILL.md
- Network accessline 127
The Streamable HTTP endpoint is http://127.0.0.1:8050/mcp. In a second activated shell run: - Network accessline 134
async def main(): async with Client("http://127.0.0.1:8050/mcp") as client: - Network accessline 135
async with Client("http://127.0.0.1:8050/mcp") as client: - Network accessline 154
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 155
tu doctor --forward http://127.0.0.1:8050/mcp --json - Network accessline 156
tu serve --share --forward http://127.0.0.1:8050/mcp --name validation-esm-if1 --workers 1 - Network accessline 208
async def main(): async with Client("http://127.0.0.1:8050/mcp") as client: - Network accessline 209
async with Client("http://127.0.0.1:8050/mcp") as client:
skills/setup-esmc-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8037/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8037/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8037/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8037/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8037/mcp --name validation-esmc --workers 1 - Network accessline 183
async def main(): arguments = json.loads(r'''{"sequence": "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"}''') async with Client("http://127.0.0.1:8037/mcp") as client: - Network accessline 184
arguments = json.loads(r'''{"sequence": "MQIFVKTLTGKTITLEVEPSDTIENVKAKIQDKEGIPPDQQRLIFAGKQLEDGRTLSDYNIQKESTLHLVLRLRGG"}''') async with Client("http://127.0.0.1:8037/mcp") as client: result = await client.call_tool("run_esmc_embe - Network accessline 185
async with Client("http://127.0.0.1:8037/mcp") as client:
skills/setup-esmfold2-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8038/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8038/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8038/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8038/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8038/mcp --name validation-esmfold2 --workers 1 - Network accessline 183
async def main(): arguments = json.loads(r'''{"chains":[{"sequence":"KKAVINGEQIRSISDLHQTLKKELALPEYYGENLDALWDALTGWVEYPLVLEWRQFEQSKQLTENGAESVLQVFREAKAEGADITIILS"}],"seed":0}''') async with Client("http://127.0.0.1:8038/mcp") as client - Network accessline 184
arguments = json.loads(r'''{"chains":[{"sequence":"KKAVINGEQIRSISDLHQTLKKELALPEYYGENLDALWDALTGWVEYPLVLEWRQFEQSKQLTENGAESVLQVFREAKAEGADITIILS"}],"seed":0}''') async with Client("http://127.0.0.1:8038/mcp") as client: result = awa - Network accessline 185
async with Client("http://127.0.0.1:8038/mcp") as client:
skills/setup-evo2-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8041/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8041/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8041/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8041/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8041/mcp --name validation-evo2 --workers 1 - Possible obfuscationline 172
{"sequence": "ACGGCTAATAATCAATACTAAAGTCACGTCGCAGCTCATCCATGCGTCCTGAAAGTCACTATTCCCAGAGTCATAATCAAATGGAACTCGTAACACAAGGTATTCTGTATCGGGAGCAGTAGCCGCCTAATTTTTTATGAGCGTCGTCGAAACAATCAGACAATCTTCTTGATAATGAAGAAGACGGTTGGGATTTACATAAGATTCCTTCTTGTACCATGAAGTC - Network accessline 183
async def main(): arguments = json.loads(r'''{"sequence": "ACGGCTAATAATCAATACTAAAGTCACGTCGCAGCTCATCCATGCGTCCTGAAAGTCACTATTCCCAGAGTCATAATCAAATGGAACTCGTAACACAAGGTATTCTGTATCGGGAGCAGTAGCCGCCTAATTTTTTATGAGCGTCGTCGAAACAATCAGACAATCTTCTTGATAATG - Network accessline 184
arguments = json.loads(r'''{"sequence": "ACGGCTAATAATCAATACTAAAGTCACGTCGCAGCTCATCCATGCGTCCTGAAAGTCACTATTCCCAGAGTCATAATCAAATGGAACTCGTAACACAAGGTATTCTGTATCGGGAGCAGTAGCCGCCTAATTTTTTATGAGCGTCGTCGAAACAATCAGACAATCTTCTTGATAATGAAGAAGACGGTTGGGATTTACA - Possible obfuscationline 184
arguments = json.loads(r'''{"sequence": "ACGGCTAATAATCAATACTAAAGTCACGTCGCAGCTCATCCATGCGTCCTGAAAGTCACTATTCCCAGAGTCATAATCAAATGGAACTCGTAACACAAGGTATTCTGTATCGGGAGCAGTAGCCGCCTAATTTTTTATGAGCGTCGTCGAAACAATCAGACAATCTTCTTGATAATGAAGAAGACGGTTGGGATTTACA - Network accessline 185
async with Client("http://127.0.0.1:8041/mcp") as client:
skills/setup-expert-feedback-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/expert-feedback > curl -fsSL -o ~/.tooluniverse/envs/expert-feedback-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/expert-feedback > curl -fsSL -o ~/.tooluniverse/envs/expert-feedback-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > ~/.local/bin/uv pip install --pytho - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/expert-feedback-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 14
> # then this model's own dependencies, as in "Create an isolated environment" below > ~/.local/bin/tu remote check expert-feedback --python ~/.tooluniverse/envs/expert-feedback/bin/python # lists anything still missing > ~/.local/bin/tu - Network accessline 15
> ~/.local/bin/tu remote check expert-feedback --python ~/.tooluniverse/envs/expert-feedback/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow expert-feedback --python ~/.tooluniverse/envs/expert-feedback/bin/pytho - Network accessline 16
> ~/.local/bin/tu serve --allow expert-feedback --python ~/.tooluniverse/envs/expert-feedback/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:9876/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:9876/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:9876/mcp") as client: - Network accessline 125
Confirm discovery contains consult_human_expert; stop on empty, duplicate, or schema-drifted discovery. Also require `curl --fail http://127.0.0.1:9877/health`; `/api/health` is not a - Network accessline 126
Also require `curl --fail http://127.0.0.1:9877/health`; `/api/health` is not a - Network accessline 134
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 135
tu doctor --forward http://127.0.0.1:9876/mcp --json - Network accessline 136
tu serve --share --forward http://127.0.0.1:9876/mcp --name validation-expert-feedback --workers 2 - Network accessline 163
async def main(): arguments = json.loads('''{"question":"Review this synthetic result.","context":"No private or patient data."}''') async with Client("http://127.0.0.1:9876/mcp") as client: - Network accessline 164
arguments = json.loads('''{"question":"Review this synthetic result.","context":"No private or patient data."}''') async with Client("http://127.0.0.1:9876/mcp") as client: result = await client.call_tool("consult_human_expert", - Network accessline 165
async with Client("http://127.0.0.1:9876/mcp") as client:
skills/setup-genie3-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8048/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8048/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8048/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8048/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8048/mcp --name validation-genie3 --workers 1 - Network accessline 183
async def main(): arguments = json.loads(r'''{"length":"60","n_designs":1,"seed":0}''') async with Client("http://127.0.0.1:8048/mcp") as client: - Network accessline 184
arguments = json.loads(r'''{"length":"60","n_designs":1,"seed":0}''') async with Client("http://127.0.0.1:8048/mcp") as client: result = await client.call_tool("genie3_design", arguments) - Network accessline 185
async with Client("http://127.0.0.1:8048/mcp") as client:
skills/setup-gpnstar-remote-tool/SKILL.md
- Network accessline 137
The Streamable HTTP endpoint is http://127.0.0.1:8057/mcp. In a second activated shell run: - Network accessline 144
async def main(): async with Client("http://127.0.0.1:8057/mcp") as client: - Network accessline 145
async with Client("http://127.0.0.1:8057/mcp") as client: - Network accessline 163
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 164
tu doctor --forward http://127.0.0.1:8057/mcp --json - Network accessline 165
tu serve --share --forward http://127.0.0.1:8057/mcp --name validation-gpnstar --workers 1 - Network accessline 196
async def main(): arguments = json.loads(r'''{"variants": [{"chrom": "1", "pos": 1425822, "ref": "C", "alt": "G"}, {"chrom": "2", "pos": 5718743, "ref": "A", "alt": "G"}, {"chrom": "3", "pos": 213423, "ref": "A", "alt": "G"}, {"chrom": - Network accessline 197
arguments = json.loads(r'''{"variants": [{"chrom": "1", "pos": 1425822, "ref": "C", "alt": "G"}, {"chrom": "2", "pos": 5718743, "ref": "A", "alt": "G"}, {"chrom": "3", "pos": 213423, "ref": "A", "alt": "G"}, {"chrom": "5", "pos": 850655, "r - Network accessline 198
async with Client("http://127.0.0.1:8057/mcp") as client:
skills/setup-harmony-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/harmony > curl -fsSL -o ~/.tooluniverse/envs/harmony-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/harmony > curl -fsSL -o ~/.tooluniverse/envs/harmony-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/harmony-requirem - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/harmony-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/harmony-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/harmony/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/harmony/bin/python -r ~/.tooluniverse/envs/harmony-tooluniverse.txt -r ~/.tooluniverse/envs/harmony-requirements.txt > ~/.local/bin/tu remote check harmony --python ~/.tooluniverse - Network accessline 15
> ~/.local/bin/tu remote check harmony --python ~/.tooluniverse/envs/harmony/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow harmony --python ~/.tooluniverse/envs/harmony/bin/python --share --detach --name "<comp - Network accessline 16
> ~/.local/bin/tu serve --allow harmony --python ~/.tooluniverse/envs/harmony/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8026/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8026/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8026/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8026/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8026/mcp --name validation-harmony --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","batch_key":"batch","n_pcs":2}''') async with Client("http://127.0.0.1:8026/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","batch_key":"batch","n_pcs":2}''') async with Client("http://127.0.0.1:8026/mcp") as client: result = await client.call_tool("run_harmony_integrate", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8026/mcp") as client:
skills/setup-immune-compass-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/immune-compass > curl -fsSL -o ~/.tooluniverse/envs/immune-compass-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/immune-compass > curl -fsSL -o ~/.tooluniverse/envs/immune-compass-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/im - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/immune-compass-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/immune-compass-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/immune_compass/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/immune-compass/bin/python -r ~/.tooluniverse/envs/immune-compass-tooluniverse.txt -r ~/.tooluniverse/envs/immune-compass-requirements.txt > ~/.local/bin/tu remote check immune-comp - Network accessline 15
> ~/.local/bin/tu remote check immune-compass --python ~/.tooluniverse/envs/immune-compass/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow immune-compass --python ~/.tooluniverse/envs/immune-compass/bin/python -- - Network accessline 16
> ~/.local/bin/tu serve --allow immune-compass --python ~/.tooluniverse/envs/immune-compass/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 124
The Streamable HTTP endpoint is http://127.0.0.1:7003/mcp. In a second activated shell run: - Network accessline 131
async def main(): async with Client("http://127.0.0.1:7003/mcp") as client: - Network accessline 132
async with Client("http://127.0.0.1:7003/mcp") as client: - Network accessline 146
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 147
tu doctor --forward http://127.0.0.1:7003/mcp --json - Network accessline 148
tu serve --share --forward http://127.0.0.1:7003/mcp --name validation-immune-compass --workers 1 - Network accessline 175
async def main(): arguments = json.loads('''{"gene_expression_data_path":"compass_gide_official_sample_1.tsv","threshold":0.5}''') async with Client("http://127.0.0.1:7003/mcp") as client: - Network accessline 176
arguments = json.loads('''{"gene_expression_data_path":"compass_gide_official_sample_1.tsv","threshold":0.5}''') async with Client("http://127.0.0.1:7003/mcp") as client: result = await client.call_tool("run_compass_prediction", - Network accessline 177
async with Client("http://127.0.0.1:7003/mcp") as client:
skills/setup-ldsc-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/ldsc > curl -fsSL -o ~/.tooluniverse/envs/ldsc-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/ldsc > curl -fsSL -o ~/.tooluniverse/envs/ldsc-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/ldsc-requirements.txt - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/ldsc-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/ldsc-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/ldsc/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/ldsc/bin/python -r ~/.tooluniverse/envs/ldsc-tooluniverse.txt -r ~/.tooluniverse/envs/ldsc-requirements.txt > ~/.local/bin/tu remote check ldsc --python ~/.tooluniverse/envs/ldsc/b - Network accessline 15
> ~/.local/bin/tu remote check ldsc --python ~/.tooluniverse/envs/ldsc/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow ldsc --python ~/.tooluniverse/envs/ldsc/bin/python --share --detach --name "<computer name>" - Network accessline 16
> ~/.local/bin/tu serve --allow ldsc --python ~/.tooluniverse/envs/ldsc/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8013/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8013/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8013/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8013/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8013/mcp --name validation-ldsc --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"sumstats_path":"tiny.sumstats.gz"}''') async with Client("http://127.0.0.1:8013/mcp") as client: - Network accessline 162
arguments = json.loads('''{"sumstats_path":"tiny.sumstats.gz"}''') async with Client("http://127.0.0.1:8013/mcp") as client: result = await client.call_tool("run_ldsc_heritability", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8013/mcp") as client:
skills/setup-liana-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/liana > curl -fsSL -o ~/.tooluniverse/envs/liana-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/liana > curl -fsSL -o ~/.tooluniverse/envs/liana-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/liana-requirements.t - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/liana-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/liana-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/liana/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/liana/bin/python -r ~/.tooluniverse/envs/liana-tooluniverse.txt -r ~/.tooluniverse/envs/liana-requirements.txt > ~/.local/bin/tu remote check liana --python ~/.tooluniverse/envs/li - Network accessline 15
> ~/.local/bin/tu remote check liana --python ~/.tooluniverse/envs/liana/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow liana --python ~/.tooluniverse/envs/liana/bin/python --share --detach --name "<computer nam - Network accessline 16
> ~/.local/bin/tu serve --allow liana --python ~/.tooluniverse/envs/liana/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8017/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8017/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8017/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8017/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8017/mcp --name validation-liana --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","cluster_key":"cell_type","top_n":2}''') async with Client("http://127.0.0.1:8017/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","cluster_key":"cell_type","top_n":2}''') async with Client("http://127.0.0.1:8017/mcp") as client: result = await client.call_tool("run_liana_cellphonedb", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8017/mcp") as client:
skills/setup-macs3-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/macs3 > curl -fsSL -o ~/.tooluniverse/envs/macs3-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/macs3 > curl -fsSL -o ~/.tooluniverse/envs/macs3-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/macs3-requirements.t - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/macs3-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/macs3-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/macs3/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/macs3/bin/python -r ~/.tooluniverse/envs/macs3-tooluniverse.txt -r ~/.tooluniverse/envs/macs3-requirements.txt > ~/.local/bin/tu remote check macs3 --python ~/.tooluniverse/envs/ma - Network accessline 15
> ~/.local/bin/tu remote check macs3 --python ~/.tooluniverse/envs/macs3/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow macs3 --python ~/.tooluniverse/envs/macs3/bin/python --share --detach --name "<computer nam - Network accessline 16
> ~/.local/bin/tu serve --allow macs3 --python ~/.tooluniverse/envs/macs3/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8021/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8021/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8021/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8021/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8021/mcp --name validation-macs3 --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"treatment":"tiny.bed","format":"BED","genome_size":"hs","top_n":2}''') async with Client("http://127.0.0.1:8021/mcp") as client: - Network accessline 162
arguments = json.loads('''{"treatment":"tiny.bed","format":"BED","genome_size":"hs","top_n":2}''') async with Client("http://127.0.0.1:8021/mcp") as client: result = await client.call_tool("run_macs3_callpeak", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8021/mcp") as client:
skills/setup-milo-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/milo > curl -fsSL -o ~/.tooluniverse/envs/milo-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/milo > curl -fsSL -o ~/.tooluniverse/envs/milo-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/milo-requirements.txt - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/milo-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/milo-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/milo/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/milo/bin/python -r ~/.tooluniverse/envs/milo-tooluniverse.txt -r ~/.tooluniverse/envs/milo-requirements.txt > ~/.local/bin/tu remote check milo --python ~/.tooluniverse/envs/milo/b - Network accessline 15
> ~/.local/bin/tu remote check milo --python ~/.tooluniverse/envs/milo/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow milo --python ~/.tooluniverse/envs/milo/bin/python --share --detach --name "<computer name>" - Network accessline 16
> ~/.local/bin/tu serve --allow milo --python ~/.tooluniverse/envs/milo/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8023/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8023/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8023/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8023/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8023/mcp --name validation-milo --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","sample_col":"sample","condition_col":"condition","n_neighbors":5}''') async with Client("http://127.0.0.1:8023/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","sample_col":"sample","condition_col":"condition","n_neighbors":5}''') async with Client("http://127.0.0.1:8023/mcp") as client: result = await client.call_tool("run_milo_diffe - Network accessline 163
async with Client("http://127.0.0.1:8023/mcp") as client:
skills/setup-mofa-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/mofa > curl -fsSL -o ~/.tooluniverse/envs/mofa-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/mofa > curl -fsSL -o ~/.tooluniverse/envs/mofa-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/mofa-requirements.txt - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/mofa-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/mofa-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/mofa/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/mofa/bin/python -r ~/.tooluniverse/envs/mofa-tooluniverse.txt -r ~/.tooluniverse/envs/mofa-requirements.txt > ~/.local/bin/tu remote check mofa --python ~/.tooluniverse/envs/mofa/b - Network accessline 15
> ~/.local/bin/tu remote check mofa --python ~/.tooluniverse/envs/mofa/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow mofa --python ~/.tooluniverse/envs/mofa/bin/python --share --detach --name "<computer name>" - Network accessline 16
> ~/.local/bin/tu serve --allow mofa --python ~/.tooluniverse/envs/mofa/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8024/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8024/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8024/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8024/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8024/mcp --name validation-mofa --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"views":{"rna":{"g1":[1,2],"g2":[3,4]},"protein":{"p1":[2,1],"p2":[4,3]}},"n_factors":1,"n_iter":10}''') async with Client("http://127.0.0.1:8024/mcp") as client: - Network accessline 162
arguments = json.loads('''{"views":{"rna":{"g1":[1,2],"g2":[3,4]},"protein":{"p1":[2,1],"p2":[4,3]}},"n_factors":1,"n_iter":10}''') async with Client("http://127.0.0.1:8024/mcp") as client: result = await client.call_tool("run_m - Network accessline 163
async with Client("http://127.0.0.1:8024/mcp") as client:
skills/setup-monocle3-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/monocle3 > curl -fsSL -o ~/.tooluniverse/envs/monocle3-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/monocle3 > curl -fsSL -o ~/.tooluniverse/envs/monocle3-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/monocle3-requi - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/monocle3-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/monocle3-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/monocle3/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/monocle3/bin/python -r ~/.tooluniverse/envs/monocle3-tooluniverse.txt -r ~/.tooluniverse/envs/monocle3-requirements.txt > ~/.local/bin/tu remote check monocle3 --python ~/.tooluniv - Network accessline 15
> ~/.local/bin/tu remote check monocle3 --python ~/.tooluniverse/envs/monocle3/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow monocle3 --python ~/.tooluniverse/envs/monocle3/bin/python --share --detach --name "< - Network accessline 16
> ~/.local/bin/tu serve --allow monocle3 --python ~/.tooluniverse/envs/monocle3/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 111
The Streamable HTTP endpoint is http://127.0.0.1:8031/mcp. In a second activated shell run: - Network accessline 118
async def main(): async with Client("http://127.0.0.1:8031/mcp") as client: - Network accessline 119
async with Client("http://127.0.0.1:8031/mcp") as client: - Network accessline 133
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 134
tu doctor --forward http://127.0.0.1:8031/mcp --json - Network accessline 135
tu serve --share --forward http://127.0.0.1:8031/mcp --name validation-monocle3 --workers 1 - Network accessline 162
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","root_cells":["cell-1"],"num_dim":2}''') async with Client("http://127.0.0.1:8031/mcp") as client: - Network accessline 163
arguments = json.loads('''{"adata_path":"tiny.h5ad","root_cells":["cell-1"],"num_dim":2}''') async with Client("http://127.0.0.1:8031/mcp") as client: result = await client.call_tool("run_monocle3_pseudotime", arguments) - Network accessline 164
async with Client("http://127.0.0.1:8031/mcp") as client:
skills/setup-mosaic-remote-tool/SKILL.md
- Network accessline 119
The Streamable HTTP endpoint is http://127.0.0.1:8055/mcp. In a second activated shell run: - Network accessline 126
async def main(): async with Client("http://127.0.0.1:8055/mcp") as client: - Network accessline 127
async with Client("http://127.0.0.1:8055/mcp") as client: - Network accessline 141
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 142
tu doctor --forward http://127.0.0.1:8055/mcp --json - Network accessline 143
tu serve --share --forward http://127.0.0.1:8055/mcp --name validation-mosaic --workers 1 - Network accessline 174
async def main(): arguments = json.loads(r'''{"target_sequence":"KKAVINGEQIRSISDLHQTLKKELALPEYYGENLDALWDALTGWVEYPLVLEWRQFEQSKQLTENGAESVLQVFREAKAEGADITIILS","binder_length":80,"steps1":10,"steps2":5,"seed":0}''') async with Client("h - Network accessline 175
arguments = json.loads(r'''{"target_sequence":"KKAVINGEQIRSISDLHQTLKKELALPEYYGENLDALWDALTGWVEYPLVLEWRQFEQSKQLTENGAESVLQVFREAKAEGADITIILS","binder_length":80,"steps1":10,"steps2":5,"seed":0}''') async with Client("http://127.0.0.1:8055/m - Network accessline 176
async with Client("http://127.0.0.1:8055/mcp") as client:
skills/setup-opendde-remote-tool/SKILL.md
- Network accessline 111
The Streamable HTTP endpoint is http://127.0.0.1:8058/mcp. In a second activated shell run: - Network accessline 118
async def main(): async with Client("http://127.0.0.1:8058/mcp") as client: - Network accessline 119
async with Client("http://127.0.0.1:8058/mcp") as client: - Network accessline 135
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 136
tu doctor --forward http://127.0.0.1:8058/mcp --json - Network accessline 137
tu serve --share --forward http://127.0.0.1:8058/mcp --name validation-opendde --workers 1 - Network accessline 160
async def main(): arguments = json.loads(r'''{"chains":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}]}''') async with Client("http://127.0.0.1:8058/mcp") as client: - Network accessline 161
arguments = json.loads(r'''{"chains":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}]}''') async with Client("http://127.0.0.1:8058/mcp") as client: result = await client.call_tool("opendde_fold", a - Network accessline 162
async with Client("http://127.0.0.1:8058/mcp") as client:
skills/setup-openfold3-remote-tool/SKILL.md
- Network accessline 122
The Streamable HTTP endpoint is http://127.0.0.1:8039/mcp. In a second activated shell run: - Network accessline 129
async def main(): async with Client("http://127.0.0.1:8039/mcp") as client: - Network accessline 130
async with Client("http://127.0.0.1:8039/mcp") as client: - Network accessline 148
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 149
tu doctor --forward http://127.0.0.1:8039/mcp --json - Network accessline 150
tu serve --share --forward http://127.0.0.1:8039/mcp --name validation-openfold3 --workers 1 - Network accessline 182
async def main(): arguments = json.loads(r'''{"chains":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}]}''') async with Client("http://127.0.0.1:8039/mcp") as client: - Network accessline 183
arguments = json.loads(r'''{"chains":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}]}''') async with Client("http://127.0.0.1:8039/mcp") as client: result = await client.call_tool("openfold3_fold", - Network accessline 184
async with Client("http://127.0.0.1:8039/mcp") as client:
skills/setup-openfold3_ob0-remote-tool/SKILL.md
- Network accessline 153
The Streamable HTTP endpoint is http://127.0.0.1:8052/mcp. In a second activated shell run: - Network accessline 160
async def main(): async with Client("http://127.0.0.1:8052/mcp") as client: - Network accessline 161
async with Client("http://127.0.0.1:8052/mcp") as client: - Network accessline 179
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 180
tu doctor --forward http://127.0.0.1:8052/mcp --json - Network accessline 181
tu serve --share --forward http://127.0.0.1:8052/mcp --name validation-openfold3-ob0 --workers 1 - Network accessline 213
async def main(): arguments = json.loads(r'''{"chains":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}]}''') async with Client("http://127.0.0.1:8052/mcp") as client: - Network accessline 214
arguments = json.loads(r'''{"chains":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}]}''') async with Client("http://127.0.0.1:8052/mcp") as client: result = await client.call_tool("openfold3_ob0_fo - Network accessline 215
async with Client("http://127.0.0.1:8052/mcp") as client:
skills/setup-paga-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/paga > curl -fsSL -o ~/.tooluniverse/envs/paga-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/paga > curl -fsSL -o ~/.tooluniverse/envs/paga-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/paga-requirements.txt - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/paga-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/paga-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/paga/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/paga/bin/python -r ~/.tooluniverse/envs/paga-tooluniverse.txt -r ~/.tooluniverse/envs/paga-requirements.txt > ~/.local/bin/tu remote check paga --python ~/.tooluniverse/envs/paga/b - Network accessline 15
> ~/.local/bin/tu remote check paga --python ~/.tooluniverse/envs/paga/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow paga --python ~/.tooluniverse/envs/paga/bin/python --share --detach --name "<computer name>" - Network accessline 16
> ~/.local/bin/tu serve --allow paga --python ~/.tooluniverse/envs/paga/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8022/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8022/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8022/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8022/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8022/mcp --name validation-paga --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","cluster_key":"cluster","threshold":0.1}''') async with Client("http://127.0.0.1:8022/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","cluster_key":"cluster","threshold":0.1}''') async with Client("http://127.0.0.1:8022/mcp") as client: result = await client.call_tool("run_paga_trajectory", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8022/mcp") as client:
skills/setup-pinnacle-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/pinnacle > curl -fsSL -o ~/.tooluniverse/envs/pinnacle-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/pinnacle > curl -fsSL -o ~/.tooluniverse/envs/pinnacle-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/pinnacle-requi - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/pinnacle-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/pinnacle-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/pinnacle/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/pinnacle/bin/python -r ~/.tooluniverse/envs/pinnacle-tooluniverse.txt -r ~/.tooluniverse/envs/pinnacle-requirements.txt > ~/.local/bin/tu remote check pinnacle --python ~/.tooluniv - Network accessline 15
> ~/.local/bin/tu remote check pinnacle --python ~/.tooluniverse/envs/pinnacle/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow pinnacle --python ~/.tooluniverse/envs/pinnacle/bin/python --share --detach --name "< - Network accessline 16
> ~/.local/bin/tu serve --allow pinnacle --python ~/.tooluniverse/envs/pinnacle/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:7001/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:7001/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:7001/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:7001/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:7001/mcp --name validation-pinnacle --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"cell_type":"B cell","max_proteins":2}''') async with Client("http://127.0.0.1:7001/mcp") as client: - Network accessline 162
arguments = json.loads('''{"cell_type":"B cell","max_proteins":2}''') async with Client("http://127.0.0.1:7001/mcp") as client: result = await client.call_tool("run_pinnacle_ppi_retrieval", arguments) - Network accessline 163
async with Client("http://127.0.0.1:7001/mcp") as client:
skills/setup-progen2-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8047/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8047/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8047/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8047/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8047/mcp --name validation-progen2 --workers 1 - Possible obfuscationline 172
{"sequence": "1MGHGVSRPPVVTLRPAVLDDCPVLWRWRNDPETRQASVDEREIPVDTHTRWFEETLKRFDRKLFIVSADGVDAGMVRLDIQDRDAAVSVNIAPEWRGRGVGPRALGCLSREAFGPLALLRMSAVVKRENAASRIAFERAGFTVVDTGGPLLHSSKARLHVVAAIQARMGSTRLPGKVLVSIAGRPTIQRIAERLAVCQELDAVAVSTSVENRDDAIADLAAHLGL - Network accessline 183
async def main(): arguments = json.loads(r'''{"sequence": "1MGHGVSRPPVVTLRPAVLDDCPVLWRWRNDPETRQASVDEREIPVDTHTRWFEETLKRFDRKLFIVSADGVDAGMVRLDIQDRDAAVSVNIAPEWRGRGVGPRALGCLSREAFGPLALLRMSAVVKRENAASRIAFERAGFTVVDTGGPLLHSSKARLHVVAAIQARMGSTRLPGK - Network accessline 184
arguments = json.loads(r'''{"sequence": "1MGHGVSRPPVVTLRPAVLDDCPVLWRWRNDPETRQASVDEREIPVDTHTRWFEETLKRFDRKLFIVSADGVDAGMVRLDIQDRDAAVSVNIAPEWRGRGVGPRALGCLSREAFGPLALLRMSAVVKRENAASRIAFERAGFTVVDTGGPLLHSSKARLHVVAAIQARMGSTRLPGKVLVSIAGRPTIQRIAERLAVCQ - Possible obfuscationline 184
arguments = json.loads(r'''{"sequence": "1MGHGVSRPPVVTLRPAVLDDCPVLWRWRNDPETRQASVDEREIPVDTHTRWFEETLKRFDRKLFIVSADGVDAGMVRLDIQDRDAAVSVNIAPEWRGRGVGPRALGCLSREAFGPLALLRMSAVVKRENAASRIAFERAGFTVVDTGGPLLHSSKARLHVVAAIQARMGSTRLPGKVLVSIAGRPTIQRIAERLAVCQ - Network accessline 185
async with Client("http://127.0.0.1:8047/mcp") as client:
skills/setup-proteinmpnn-remote-tool/SKILL.md
- Network accessline 232
tu doctor --forward http://127.0.0.1:8049/mcp --json - Network accessline 233
tu serve --share --forward http://127.0.0.1:8049/mcp --name validation-proteinmpnn --workers 1 - Network accessline 238
The Streamable HTTP endpoint is http://127.0.0.1:8049/mcp. In a second - Network accessline 271
if not key.startswith("_") } async with Client("http://127.0.0.1:8049/mcp") as client: - Network accessline 272
} async with Client("http://127.0.0.1:8049/mcp") as client: result = await client.call_tool("proteinmpnn_design", arguments) - Network accessline 273
async with Client("http://127.0.0.1:8049/mcp") as client:
skills/setup-protenix_v1-remote-tool/SKILL.md
- Network accessline 126
The Streamable HTTP endpoint is http://127.0.0.1:8046/mcp. In a second activated shell run: - Network accessline 133
async def main(): async with Client("http://127.0.0.1:8046/mcp") as client: - Network accessline 134
async with Client("http://127.0.0.1:8046/mcp") as client: - Network accessline 152
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 153
tu doctor --forward http://127.0.0.1:8046/mcp --json - Network accessline 154
tu serve --share --forward http://127.0.0.1:8046/mcp --name validation-protenix_v1 --workers 1 - Network accessline 186
async def main(): arguments = json.loads(r'''{"chains":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}]}''') async with Client("http://127.0.0.1:8046/mcp") as client: - Network accessline 187
arguments = json.loads(r'''{"chains":[{"molecule_type":"protein","sequence":"LSDEDFKAVFGMTRSAFANLPLWKQQNLKKEKGLF"}]}''') async with Client("http://127.0.0.1:8046/mcp") as client: result = await client.call_tool("protenix_v1_fold - Network accessline 188
async with Client("http://127.0.0.1:8046/mcp") as client:
skills/setup-rfd4_proteina-remote-tool/SKILL.md
- Network accessline 50
**Two resolver traps are already recorded in that lock's header, and both cost a failed install to find.** `uv` considers only the first index that carries a package, so adding `--extra-index-url https://download.pytorch.org/whl/cu132` - Network accessline 51
a failed install to find.** `uv` considers only the first index that carries a package, so adding `--extra-index-url https://download.pytorch.org/whl/cu132` for the CUDA 13.2 wheels makes it refuse to resolve `idna` and `cuda-python` - Network accessline 52
package, so adding `--extra-index-url https://download.pytorch.org/whl/cu132` - Network accessline 135
The Streamable HTTP endpoint is http://127.0.0.1:8062/mcp. In a second activated shell run: - Network accessline 142
async def main(): async with Client("http://127.0.0.1:8062/mcp") as client: - Network accessline 143
async with Client("http://127.0.0.1:8062/mcp") as client: - Network accessline 163
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 164
tu doctor --forward http://127.0.0.1:8062/mcp --json - Network accessline 165
tu serve --share --forward http://127.0.0.1:8062/mcp --name validation-rfd4_proteina --workers 1 - Network accessline 214
async def main(): async with Client("http://127.0.0.1:8062/mcp") as client: - Network accessline 215
async with Client("http://127.0.0.1:8062/mcp") as client:
skills/setup-rfdiffusion3-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8040/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8040/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8040/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8040/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8040/mcp --name validation-rfdiffusion3 --workers 1 - Network accessline 183
async def main(): arguments = json.loads(r'''{"length":"60","n_designs":1,"seed":0}''') async with Client("http://127.0.0.1:8040/mcp") as client: - Network accessline 184
arguments = json.loads(r'''{"length":"60","n_designs":1,"seed":0}''') async with Client("http://127.0.0.1:8040/mcp") as client: result = await client.call_tool("rfdiffusion3_design", arguments) - Network accessline 185
async with Client("http://127.0.0.1:8040/mcp") as client:
skills/setup-rosettafold3-remote-tool/SKILL.md
- Network accessline 123
The Streamable HTTP endpoint is http://127.0.0.1:8034/mcp. In a second activated shell run: - Network accessline 130
async def main(): async with Client("http://127.0.0.1:8034/mcp") as client: - Network accessline 131
async with Client("http://127.0.0.1:8034/mcp") as client: - Network accessline 149
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 150
tu doctor --forward http://127.0.0.1:8034/mcp --json - Network accessline 151
tu serve --share --forward http://127.0.0.1:8034/mcp --name validation-rosettafold3 --workers 1 - Network accessline 183
async def main(): arguments = json.loads(r'''{"spec": "[{\"name\": \"barstar\", \"components\": [{\"seq\": \"KKAVINGEQIRSISDLHQTLKKELALPEYYGENLDALWDALTGWVEYPLVLEWRQFEQSKQLTENGAESVLQVFREAKAEGADITIILS\", \"chain_id\": \"A\", \"chain_type\ - Network accessline 184
arguments = json.loads(r'''{"spec": "[{\"name\": \"barstar\", \"components\": [{\"seq\": \"KKAVINGEQIRSISDLHQTLKKELALPEYYGENLDALWDALTGWVEYPLVLEWRQFEQSKQLTENGAESVLQVFREAKAEGADITIILS\", \"chain_id\": \"A\", \"chain_type\": \"polypeptide(L)\"} - Network accessline 185
async with Client("http://127.0.0.1:8034/mcp") as client:
skills/setup-scanvi-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/scanvi > curl -fsSL -o ~/.tooluniverse/envs/scanvi-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/scanvi > curl -fsSL -o ~/.tooluniverse/envs/scanvi-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/scanvi-requirement - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/scanvi-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/scanvi-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/scanvi/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/scanvi/bin/python -r ~/.tooluniverse/envs/scanvi-tooluniverse.txt -r ~/.tooluniverse/envs/scanvi-requirements.txt > ~/.local/bin/tu remote check scanvi --python ~/.tooluniverse/env - Network accessline 15
> ~/.local/bin/tu remote check scanvi --python ~/.tooluniverse/envs/scanvi/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow scanvi --python ~/.tooluniverse/envs/scanvi/bin/python --share --detach --name "<computer - Network accessline 16
> ~/.local/bin/tu serve --allow scanvi --python ~/.tooluniverse/envs/scanvi/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8027/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8027/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8027/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8027/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8027/mcp --name validation-scanvi --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","labels_key":"label","unlabeled_category":"Unknown","scvi_epochs":1,"scanvi_epochs":1}''') async with Client("http://127.0.0.1:8027/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","labels_key":"label","unlabeled_category":"Unknown","scvi_epochs":1,"scanvi_epochs":1}''') async with Client("http://127.0.0.1:8027/mcp") as client: result = await client.call_ - Network accessline 163
async with Client("http://127.0.0.1:8027/mcp") as client:
skills/setup-scrublet-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/scrublet > curl -fsSL -o ~/.tooluniverse/envs/scrublet-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/scrublet > curl -fsSL -o ~/.tooluniverse/envs/scrublet-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/scrublet-requi - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/scrublet-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/scrublet-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/scrublet/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/scrublet/bin/python -r ~/.tooluniverse/envs/scrublet-tooluniverse.txt -r ~/.tooluniverse/envs/scrublet-requirements.txt > ~/.local/bin/tu remote check scrublet --python ~/.tooluniv - Network accessline 15
> ~/.local/bin/tu remote check scrublet --python ~/.tooluniverse/envs/scrublet/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow scrublet --python ~/.tooluniverse/envs/scrublet/bin/python --share --detach --name "< - Network accessline 16
> ~/.local/bin/tu serve --allow scrublet --python ~/.tooluniverse/envs/scrublet/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8015/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8015/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8015/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8015/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8015/mcp --name validation-scrublet --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","expected_doublet_rate":0.06}''') async with Client("http://127.0.0.1:8015/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","expected_doublet_rate":0.06}''') async with Client("http://127.0.0.1:8015/mcp") as client: result = await client.call_tool("run_scrublet_doublets", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8015/mcp") as client:
skills/setup-scvelo-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/scvelo > curl -fsSL -o ~/.tooluniverse/envs/scvelo-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/scvelo > curl -fsSL -o ~/.tooluniverse/envs/scvelo-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/scvelo-requirement - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/scvelo-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/scvelo-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/scvelo/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/scvelo/bin/python -r ~/.tooluniverse/envs/scvelo-tooluniverse.txt -r ~/.tooluniverse/envs/scvelo-requirements.txt > ~/.local/bin/tu remote check scvelo --python ~/.tooluniverse/env - Network accessline 15
> ~/.local/bin/tu remote check scvelo --python ~/.tooluniverse/envs/scvelo/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow scvelo --python ~/.tooluniverse/envs/scvelo/bin/python --share --detach --name "<computer - Network accessline 16
> ~/.local/bin/tu serve --allow scvelo --python ~/.tooluniverse/envs/scvelo/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8025/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8025/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8025/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8025/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8025/mcp --name validation-scvelo --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","mode":"stochastic"}''') async with Client("http://127.0.0.1:8025/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","mode":"stochastic"}''') async with Client("http://127.0.0.1:8025/mcp") as client: result = await client.call_tool("run_scvelo_velocity", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8025/mcp") as client:
skills/setup-scvi-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/scvi > curl -fsSL -o ~/.tooluniverse/envs/scvi-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/scvi > curl -fsSL -o ~/.tooluniverse/envs/scvi-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/scvi-requirements.txt - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/scvi-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/scvi-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/scvi/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/scvi/bin/python -r ~/.tooluniverse/envs/scvi-tooluniverse.txt -r ~/.tooluniverse/envs/scvi-requirements.txt > ~/.local/bin/tu remote check scvi --python ~/.tooluniverse/envs/scvi/b - Network accessline 15
> ~/.local/bin/tu remote check scvi --python ~/.tooluniverse/envs/scvi/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow scvi --python ~/.tooluniverse/envs/scvi/bin/python --share --detach --name "<computer name>" - Network accessline 16
> ~/.local/bin/tu serve --allow scvi --python ~/.tooluniverse/envs/scvi/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8010/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8010/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8010/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8010/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8010/mcp --name validation-scvi --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","n_latent":2,"max_epochs":1}''') async with Client("http://127.0.0.1:8010/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","n_latent":2,"max_epochs":1}''') async with Client("http://127.0.0.1:8010/mcp") as client: result = await client.call_tool("run_scvi_integration", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8010/mcp") as client:
skills/setup-singler-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/singler > curl -fsSL -o ~/.tooluniverse/envs/singler-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/singler > curl -fsSL -o ~/.tooluniverse/envs/singler-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/singler-requirem - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/singler-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/singler-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/singler/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/singler/bin/python -r ~/.tooluniverse/envs/singler-tooluniverse.txt -r ~/.tooluniverse/envs/singler-requirements.txt > ~/.local/bin/tu remote check singler --python ~/.tooluniverse - Network accessline 15
> ~/.local/bin/tu remote check singler --python ~/.tooluniverse/envs/singler/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow singler --python ~/.tooluniverse/envs/singler/bin/python --share --detach --name "<comp - Network accessline 16
> ~/.local/bin/tu serve --allow singler --python ~/.tooluniverse/envs/singler/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 111
The Streamable HTTP endpoint is http://127.0.0.1:8029/mcp. In a second activated shell run: - Network accessline 118
async def main(): async with Client("http://127.0.0.1:8029/mcp") as client: - Network accessline 119
async with Client("http://127.0.0.1:8029/mcp") as client: - Network accessline 133
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 134
tu doctor --forward http://127.0.0.1:8029/mcp --json - Network accessline 135
tu serve --share --forward http://127.0.0.1:8029/mcp --name validation-singler --workers 1 - Network accessline 162
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","celldex_ref":"HumanPrimaryCellAtlasData","ref_label_field":"label.main"}''') async with Client("http://127.0.0.1:8029/mcp") as client: - Network accessline 163
arguments = json.loads('''{"adata_path":"tiny.h5ad","celldex_ref":"HumanPrimaryCellAtlasData","ref_label_field":"label.main"}''') async with Client("http://127.0.0.1:8029/mcp") as client: result = await client.call_tool("run_sin - Network accessline 164
async with Client("http://127.0.0.1:8029/mcp") as client:
skills/setup-slingshot-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/slingshot > curl -fsSL -o ~/.tooluniverse/envs/slingshot-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/slingshot > curl -fsSL -o ~/.tooluniverse/envs/slingshot-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/slingshot-re - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/slingshot-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/slingshot-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/slingshot/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/slingshot/bin/python -r ~/.tooluniverse/envs/slingshot-tooluniverse.txt -r ~/.tooluniverse/envs/slingshot-requirements.txt > ~/.local/bin/tu remote check slingshot --python ~/.tool - Network accessline 15
> ~/.local/bin/tu remote check slingshot --python ~/.tooluniverse/envs/slingshot/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow slingshot --python ~/.tooluniverse/envs/slingshot/bin/python --share --detach --nam - Network accessline 16
> ~/.local/bin/tu serve --allow slingshot --python ~/.tooluniverse/envs/slingshot/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 111
The Streamable HTTP endpoint is http://127.0.0.1:8030/mcp. In a second activated shell run: - Network accessline 118
async def main(): async with Client("http://127.0.0.1:8030/mcp") as client: - Network accessline 119
async with Client("http://127.0.0.1:8030/mcp") as client: - Network accessline 133
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 134
tu doctor --forward http://127.0.0.1:8030/mcp --json - Network accessline 135
tu serve --share --forward http://127.0.0.1:8030/mcp --name validation-slingshot --workers 1 - Network accessline 162
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","cluster_key":"cluster","embedding_key":"X_umap"}''') async with Client("http://127.0.0.1:8030/mcp") as client: - Network accessline 163
arguments = json.loads('''{"adata_path":"tiny.h5ad","cluster_key":"cluster","embedding_key":"X_umap"}''') async with Client("http://127.0.0.1:8030/mcp") as client: result = await client.call_tool("run_slingshot_trajectory", argu - Network accessline 164
async with Client("http://127.0.0.1:8030/mcp") as client:
skills/setup-squidpy-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/squidpy > curl -fsSL -o ~/.tooluniverse/envs/squidpy-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/squidpy > curl -fsSL -o ~/.tooluniverse/envs/squidpy-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/squidpy-requirem - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/squidpy-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/squidpy-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/squidpy/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/squidpy/bin/python -r ~/.tooluniverse/envs/squidpy-tooluniverse.txt -r ~/.tooluniverse/envs/squidpy-requirements.txt > ~/.local/bin/tu remote check squidpy --python ~/.tooluniverse - Network accessline 15
> ~/.local/bin/tu remote check squidpy --python ~/.tooluniverse/envs/squidpy/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow squidpy --python ~/.tooluniverse/envs/squidpy/bin/python --share --detach --name "<comp - Network accessline 16
> ~/.local/bin/tu serve --allow squidpy --python ~/.tooluniverse/envs/squidpy/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8016/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8016/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8016/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8016/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8016/mcp --name validation-squidpy --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"adata_path":"tiny.h5ad","cluster_key":"cluster","n_neighs":2}''') async with Client("http://127.0.0.1:8016/mcp") as client: - Network accessline 162
arguments = json.loads('''{"adata_path":"tiny.h5ad","cluster_key":"cluster","n_neighs":2}''') async with Client("http://127.0.0.1:8016/mcp") as client: result = await client.call_tool("run_squidpy_nhood_enrichment", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8016/mcp") as client:
skills/setup-tangram-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/tangram > curl -fsSL -o ~/.tooluniverse/envs/tangram-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/tangram > curl -fsSL -o ~/.tooluniverse/envs/tangram-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/envs/tangram-requirem - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/tangram-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/tangram-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/tangram/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/tangram/bin/python -r ~/.tooluniverse/envs/tangram-tooluniverse.txt -r ~/.tooluniverse/envs/tangram-requirements.txt > ~/.local/bin/tu remote check tangram --python ~/.tooluniverse - Network accessline 15
> ~/.local/bin/tu remote check tangram --python ~/.tooluniverse/envs/tangram/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow tangram --python ~/.tooluniverse/envs/tangram/bin/python --share --detach --name "<comp - Network accessline 16
> ~/.local/bin/tu serve --allow tangram --python ~/.tooluniverse/envs/tangram/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8018/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8018/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8018/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8018/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8018/mcp --name validation-tangram --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"sc_path":"reference.h5ad","sp_path":"spatial.h5ad","cluster_label":"cell_type","num_epochs":1}''') async with Client("http://127.0.0.1:8018/mcp") as client: - Network accessline 162
arguments = json.loads('''{"sc_path":"reference.h5ad","sp_path":"spatial.h5ad","cluster_label":"cell_type","num_epochs":1}''') async with Client("http://127.0.0.1:8018/mcp") as client: result = await client.call_tool("run_tangra - Network accessline 163
async with Client("http://127.0.0.1:8018/mcp") as client:
skills/setup-tooluniverse/API_KEYS_REFERENCE.md
- Access to secretsline 9
[Credential-related source omitted] - Network accessline 14
- **What it does**: Increases PubMed API rate limit from 3 requests/second to 10 requests/second. Without it, heavy literature searches may be throttled. - **How to get it**: 1. Go to https://account.ncbi.nlm.nih.gov/ and sign in (via Goo - Network accessline 15
- **How to get it**: 1. Go to https://account.ncbi.nlm.nih.gov/ and sign in (via Google, ORCiD, eRA Commons, or Login.gov) 2. Go to Settings at https://account.ncbi.nlm.nih.gov/settings/ - Network accessline 16
1. Go to https://account.ncbi.nlm.nih.gov/ and sign in (via Google, ORCiD, eRA Commons, or Login.gov) - Network accessline 17
2. Go to Settings at https://account.ncbi.nlm.nih.gov/settings/ - Access to secretsline 21
[Credential-related source omitted] - Access to secretsline 23
[Credential-related source omitted] - Network accessline 28
- **What it does**: Provides access to GPU-accelerated bioinformatics models hosted by NVIDIA. Covers protein structure prediction, molecular docking, and sequence analysis. - **How to get it**: 1. Go to https://build.nvidia.com - Network accessline 29
- **How to get it**: 1. Go to https://build.nvidia.com 2. Sign in or create a free NVIDIA account - Network accessline 30
1. Go to https://build.nvidia.com - Access to secretsline 35
[Credential-related source omitted] - Access to secretsline 38
[Credential-related source omitted] - Network accessline 43
- **What it does**: Enables querying the BioGRID database for protein-protein interactions, genetic interactions, and chemical associations. - **How to get it**: 1. Go to https://webservice.thebiogrid.org/ - Network accessline 44
- **How to get it**: 1. Go to https://webservice.thebiogrid.org/ 2. Click "Register" to create a free account - Network accessline 45
1. Go to https://webservice.thebiogrid.org/ - Access to secretsline 49
[Credential-related source omitted] - Access to secretsline 51
[Credential-related source omitted] - Network accessline 56
- **What it does**: Provides access to one of the largest collections of gene-disease associations, integrating data from expert-curated repositories, GWAS catalogs, and animal models. - **How to get it**: 1. Go to https://disgenet.com/ac - Network accessline 57
- **How to get it**: 1. Go to https://disgenet.com/academic-apply (for academic/non-profit use) 2. Fill out the application with your institutional email - Network accessline 58
1. Go to https://disgenet.com/academic-apply (for academic/non-profit use) - Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 70
[Credential-related source omitted] - Network accessline 75
- **What it does**: Provides access to the authoritative database of human genes and genetic phenotypes, particularly for inherited diseases. - **How to get it**: 1. Go to https://omim.org/api - Network accessline 76
- **How to get it**: 1. Go to https://omim.org/api 2. Click "Request API Key" - Network accessline 77
1. Go to https://omim.org/api - Access to secretsline 81
[Credential-related source omitted] - Network accessline 89
- **What it does**: Provides access to MSK's precision oncology knowledge base with FDA-recognized biomarker-drug associations, levels of evidence for therapeutic implications. - **How to get it**: 1. Go to https://www.oncokb.org/apiAcces - Network accessline 90
- **How to get it**: 1. Go to https://www.oncokb.org/apiAccess 2. Click "Request API Access" - Network accessline 91
1. Go to https://www.oncokb.org/apiAccess - Access to secretsline 96
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Network accessline 104
- **What it does**: Maps between medical terminologies, finds concept definitions, and navigates hierarchical relationships between medical concepts. - **How to get it**: 1. Go to https://uts.nlm.nih.gov/uts/ - Network accessline 105
- **How to get it**: 1. Go to https://uts.nlm.nih.gov/uts/ 2. Click "Sign Up" to create a UMLS Terminology Services account - Network accessline 106
1. Go to https://uts.nlm.nih.gov/uts/ - Access to secretsline 110
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Network accessline 118
- **What it does**: Enables searching and retrieving US patent documents, patent claims, and patent family information. - **How to get it**: 1. Create a USPTO account at https://my.uspto.gov/ (if you don't have one) - Network accessline 119
- **How to get it**: 1. Create a USPTO account at https://my.uspto.gov/ (if you don't have one) 2. Go to the API manager at https://account.uspto.gov/api-manager/ - Network accessline 120
1. Create a USPTO account at https://my.uspto.gov/ (if you don't have one) - Network accessline 121
2. Go to the API manager at https://account.uspto.gov/api-manager/ - Access to secretsline 124
[Credential-related source omitted] - Access to secretsline 126
[Credential-related source omitted] - Network accessline 131
- **What it does**: Provides an isolated authenticated quota (new keys currently start at 1 request/second) instead of relying on the dynamically throttled anonymous pool. It also enables authentication-required endpoints. - **How to get it - Network accessline 132
- **How to get it**: 1. Go to https://www.semanticscholar.org/product/api 2. Click "Request API Key" - Network accessline 133
1. Go to https://www.semanticscholar.org/product/api - Access to secretsline 137
[Credential-related source omitted] - Access to secretsline 139
[Credential-related source omitted] - Network accessline 144
- **What it does**: Increases openFDA rate limit from 240 requests/minute to 1000 requests/minute. Useful for drug safety signal analysis. - **How to get it**: 1. Go to https://open.fda.gov/apis/authentication/ - Network accessline 145
- **How to get it**: 1. Go to https://open.fda.gov/apis/authentication/ 2. Enter your email to request a key - Network accessline 146
1. Go to https://open.fda.gov/apis/authentication/ - Access to secretsline 149
[Credential-related source omitted] - Network accessline 156
- **What it does**: Provides access to the world's most comprehensive enzyme database with kinetic parameters (Km, Vmax, kcat), substrate/product data, and enzyme classifications. - **How to get it**: 1. Go to https://brenda-enzymes.org/r - Network accessline 157
- **How to get it**: 1. Go to https://brenda-enzymes.org/register.php 2. Fill in email, password, name, and scientific field - Network accessline 158
1. Go to https://brenda-enzymes.org/register.php - Access to secretsline 166
[Credential-related source omitted] - Network accessline 171
- **What it does**: Provides access to Mouser's comprehensive catalog of electronic components, including ICs, resistors, capacitors, microcontrollers, and more. Includes pricing, availability, datasheets, and specifications. - **How to get - Network accessline 172
- **How to get it**: 1. Go to https://www.mouser.com/api-search/ 2. Click "Sign Up For Search API" - Network accessline 173
1. Go to https://www.mouser.com/api-search/ - Access to secretsline 177
[Credential-related source omitted] - Access to secretsline 180
[Credential-related source omitted] - Network accessline 185
- **What it does**: Provides access to Digi-Key's extensive catalog of electronic components with detailed specifications, pricing, inventory, parametric search, and technical documentation. - **How to get it**: 1. Go to https://developer - Network accessline 186
- **How to get it**: 1. Go to https://developer.digikey.com/ 2. Click "Register" to create a free developer account - Network accessline 187
1. Go to https://developer.digikey.com/ - Network accessline 189
3. After login, go to "Organization" → "Production Apps" 4. Click "Create Production App" 5. Fill in app details (name, description, OAuth callback URL: `https://localhost/callback`) - Network accessline 190
4. Click "Create Production App" 5. Fill in app details (name, description, OAuth callback URL: `https://localhost/callback`) 6. After creation, copy the **Client ID** and **Client Secret** - Network accessline 191
5. Fill in app details (name, description, OAuth callback URL: `https://localhost/callback`) - Access to secretsline 193
[Credential-related source omitted] - Access to secretsline 205
[Credential-related source omitted] - Network accessline 209
- **What it does**: Powers agentic tools using Google's Gemini models. Good default choice due to generous free tier. - **How to get it**: 1. Go to https://aistudio.google.com/apikey - Network accessline 210
- **How to get it**: 1. Go to https://aistudio.google.com/apikey 2. Sign in with your Google account - Network accessline 211
1. Go to https://aistudio.google.com/apikey - Access to secretsline 215
[Credential-related source omitted] - Access to secretsline 218
[Credential-related source omitted] - Network accessline 222
- **What it does**: Provides access to 100+ LLM models through a single API. Pay-per-use pricing, good flexibility. - **How to get it**: 1. Go to https://openrouter.ai/ - Network accessline 223
- **How to get it**: 1. Go to https://openrouter.ai/ 2. Sign up or log in - Network accessline 224
1. Go to https://openrouter.ai/ - Network accessline 225
2. Sign up or log in 3. Go to https://openrouter.ai/keys 4. Click "Create Key" - Network accessline 226
3. Go to https://openrouter.ai/keys - Access to secretsline 229
[Credential-related source omitted] - Access to secretsline 231
[Credential-related source omitted] - Network accessline 235
- **What it does**: Powers embedding-based tool finding and can serve as an LLM provider for agentic features. - **How to get it**: 1. Go to https://platform.openai.com/ - Network accessline 236
- **How to get it**: 1. Go to https://platform.openai.com/ 2. Sign up or log in - Network accessline 237
1. Go to https://platform.openai.com/ - Access to secretsline 242
[Credential-related source omitted] - Access to secretsline 244
[Credential-related source omitted] - Network accessline 248
- **What it does**: Same capabilities as OpenAI but through Azure infrastructure. Preferred for enterprise/institutional users. - **How to get it**: 1. Go to Azure Portal (https://portal.azure.com) - Network accessline 249
- **How to get it**: 1. Go to Azure Portal (https://portal.azure.com) 2. Create or navigate to your Azure OpenAI resource - Network accessline 250
1. Go to Azure Portal (https://portal.azure.com) - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 257
[Credential-related source omitted] - Network accessline 261
- **What it does**: Enables Claude-based features. - **How to get it**: 1. Go to https://console.anthropic.com/ - Network accessline 262
- **How to get it**: 1. Go to https://console.anthropic.com/ 2. Sign up or log in - Network accessline 263
1. Go to https://console.anthropic.com/ - Access to secretsline 267
[Credential-related source omitted] - Network accessline 273
- **What it does**: Enables access to gated HuggingFace models and datasets, and the HF Inference API for embeddings. - **How to get it**: 1. Go to https://huggingface.co/settings/tokens - Network accessline 274
- **How to get it**: 1. Go to https://huggingface.co/settings/tokens 2. Sign up or log in - Network accessline 275
1. Go to https://huggingface.co/settings/tokens - Access to secretsline 280
[Credential-related source omitted] - Access to secretsline 289
[Credential-related source omitted] - Access to secretsline 290
[Credential-related source omitted] - Access to secretsline 291
[Credential-related source omitted] - Access to secretsline 292
[Credential-related source omitted] - Access to secretsline 293
[Credential-related source omitted] - Access to secretsline 295
[Credential-related source omitted] - Access to secretsline 296
[Credential-related source omitted] - Access to secretsline 297
[Credential-related source omitted] - Access to secretsline 298
[Credential-related source omitted]
skills/setup-tooluniverse/CLAUDE_DESKTOP.md
- Network accessline 5
Claude Desktop is a GUI app. It launches in a clean environment and **does not inherit your shell's PATH**. This means if `uvx` lives in `~/.local/bin/uvx` (the default for the uv curl installer), Claude Desktop cannot find it — and will sh - Network accessline 19
If you already installed uv via the curl installer, also run: - Network accessline 55
- Homebrew Apple Silicon: `/opt/homebrew/bin/uvx` - Homebrew Intel: `/usr/local/bin/uvx` - curl installer: `/Users/yourname/.local/bin/uvx` - Network accessline 57
- curl installer: `/Users/yourname/.local/bin/uvx` - Dangerous shell operationline 81
sudo ln -sf "$(which uvx)" /usr/local/bin/uvx - Network accessline 109
If the user installed uv via curl (not Homebrew), replace `'command': 'uvx'` with `'command': '/Users/yourname/.local/bin/uvx'` using their actual path from `which uvx`. - Network accessline 149
If `npx` is not found, install Node.js from [nodejs.org](https://nodejs.org) (LTS version includes npx), then retry. - Network accessline 153
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 157
rm -rf /tmp/tu-skills - Network accessline 178
✅ **Pass**: You see folders like `tooluniverse`, `tooluniverse-drug-research`, etc. → skills are ready. ❌ **Fail**: Nothing listed → wrong directory or install didn't complete. Check [SKILLS_CATALOG.md](https://raw.githubusercontent.com/mim - Network accessline 179
❌ **Fail**: Nothing listed → wrong directory or install didn't complete. Check [SKILLS_CATALOG.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/SKILLS_CATALOG.md) and retry.
skills/setup-tooluniverse/EXAMPLES.md
- Network accessline 111
git clone https://github.com/mims-harvard/ToolUniverse.git - Dangerous shell operationline 232
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 232
curl -LsSf https://astral.sh/uv/install.sh | sh - Dangerous shell operationline 290
sudo apt install python3.12 python3.12-pip - Access to secretsline 373
[Credential-related source omitted] - Access to secretsline 374
[Credential-related source omitted] - Access to secretsline 375
[Credential-related source omitted] - Access to secretsline 388
[Credential-related source omitted] - Access to secretsline 389
[Credential-related source omitted]
skills/setup-tooluniverse/GITHUB_ISSUE.md
- Network accessline 44
> > **Step 1.** Open this link in your browser: > https://github.com/mims-harvard/ToolUniverse/issues/new - Network accessline 45
> **Step 1.** Open this link in your browser: > https://github.com/mims-harvard/ToolUniverse/issues/new > - Network accessline 46
> https://github.com/mims-harvard/ToolUniverse/issues/new
skills/setup-tooluniverse/INSTALL.md
- Network accessline 23
# Install uv first curl -LsSf https://astral.sh/uv/install.sh | sh - Dangerous shell operationline 24
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 24
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 42
git clone https://github.com/mims-harvard/ToolUniverse.git - Dangerous shell operationline 97
sudo apt update - Dangerous shell operationline 98
sudo apt install python3.12 python3.12-pip - Network accessline 104
Download from [python.org](https://www.python.org/downloads/) and select version 3.12. - Dangerous shell operationline 182
rm -rf ~/.cache/tooluniverse
skills/setup-tooluniverse/MCP_CONFIG.md
- Network accessline 65
For full Trae setup instructions (including manual write fallback and verification), see [TRAE.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TRAE.md). - Access to secretsline 253
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 255
[Credential-related source omitted] - Access to secretsline 259
[Credential-related source omitted] - Access to secretsline 268
[Credential-related source omitted] - Access to secretsline 274
[Credential-related source omitted] - Network accessline 378
- Verify uvx works: `uvx --version` - Test ToolUniverse directly: `uvx tooluniverse --help` - If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - Network accessline 379
- Test ToolUniverse directly: `uvx tooluniverse --help` - If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - For pip-based installs: `pip show tooluniverse` and `which tooluniverse-smcp-stdio` - Dangerous shell operationline 380
- If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - Network accessline 380
- If uvx is missing, install uv: `curl -LsSf https://astral.sh/uv/install.sh | sh` - Access to secretsline 436
[Credential-related source omitted]
skills/setup-tooluniverse/SKILL.md
- Network accessline 42
**If Chat mode**, ask which app (AskQuestion): Cursor, Claude Desktop, VS Code/Copilot, Windsurf, Claude Code, Gemini CLI, Codex, Cline/Trae/Antigravity/OpenCode. "I don't have any" → Recommend [Claude Desktop](https://claude.ai/download). - Dangerous shell operationline 51
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 51
curl -LsSf https://astral.sh/uv/install.sh | sh - Access to secretsline 139
[Credential-related source omitted] - Network accessline 153
Read https://aiscientist.tools/setup.md and set up ToolUniverse for me. - Network accessline 240
| Key | Unlocks | Free? | Registration | |-----|---------|-------|-------------| | `NCBI_API_KEY` | PubMed (rate limit 3→10/s) | Yes | https://account.ncbi.nlm.nih.gov/settings/ | - Network accessline 241
|-----|---------|-------|-------------| | `NCBI_API_KEY` | PubMed (rate limit 3→10/s) | Yes | https://account.ncbi.nlm.nih.gov/settings/ | | `NVIDIA_API_KEY` | 16 tools: AlphaFold2, docking, genomics | Yes | https://build.nvidia.com | - Network accessline 242
[Credential-related source omitted] - Access to secretsline 242
[Credential-related source omitted] - Network accessline 243
[Credential-related source omitted] - Access to secretsline 243
[Credential-related source omitted] - Network accessline 244
[Credential-related source omitted] - Access to secretsline 244
[Credential-related source omitted] - Network accessline 245
[Credential-related source omitted] - Access to secretsline 245
[Credential-related source omitted] - Network accessline 249
| Key | Unlocks | Registration | |-----|---------|-------------| | `DISGENET_API_KEY` | Gene-disease associations | https://disgenet.com/academic-apply | - Network accessline 250
|-----|---------|-------------| | `DISGENET_API_KEY` | Gene-disease associations | https://disgenet.com/academic-apply | | `OMIM_API_KEY` | Mendelian/rare disease | https://omim.org/api | - Network accessline 251
[Credential-related source omitted] - Access to secretsline 251
[Credential-related source omitted] - Network accessline 252
[Credential-related source omitted] - Access to secretsline 252
[Credential-related source omitted] - Network accessline 253
| `ONCOKB_API_TOKEN` | Precision oncology | https://www.oncokb.org/apiAccess | - Network accessline 254
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 264
[Credential-related source omitted] - Access to secretsline 270
[Credential-related source omitted] - Access to secretsline 271
[Credential-related source omitted] - Network accessline 300
git clone --depth 1 https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 313
Clean up: `rm -rf /tmp/tu-skills` - Access to secretsline 393
[Credential-related source omitted] - Access to secretsline 394
[Credential-related source omitted] - Network accessline 431
Still stuck? [GitHub issues](https://github.com/mims-harvard/ToolUniverse/issues) or email [Shanghua Gao](mailto:shanghuagao@gmail.com). - Network accessline 436
- **Upgrade**: `uv cache clean tooluniverse` + restart - **All scientific API keys are free** - **Skills**: https://github.com/mims-harvard/ToolUniverse/tree/main/skills - Network accessline 438
- **Skills**: https://github.com/mims-harvard/ToolUniverse/tree/main/skills
skills/setup-tooluniverse/TRAE.md
- Dangerous shell operationline 20
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 20
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 26
powershell -c "irm https://astral.sh/uv/install.ps1 | iex" - Network accessline 35
This should print help text. If it fails, see [TROUBLESHOOTING.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TROUBLESHOOTING.md). - Network accessline 129
If `npx` is not found, install Node.js from [nodejs.org](https://nodejs.org) (the LTS version includes npm and npx), then retry. - Network accessline 133
If `git` is not found, install it from [git-scm.com](https://git-scm.com), then retry. - Network accessline 137
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 140
rm -rf /tmp/tu-skills - Network accessline 145
# Run each line separately — PowerShell does not support && like bash git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git "$env:TEMP\tu-skills" - Network accessline 146
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git "$env:TEMP\tu-skills" - Network accessline 154
> Note: The exact skills directory for Trae may vary. Check [SKILLS_CATALOG.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/SKILLS_CATALOG.md) if skills don't activate after installation. - Network accessline 198
2. `uvx tooluniverse --help` works in your **system terminal** (not Trae's terminal) 3. Trae was fully restarted 4. Fetch [TROUBLESHOOTING.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TROUBL - Network accessline 200
4. Fetch [TROUBLESHOOTING.md](https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/skills/setup-tooluniverse/TROUBLESHOOTING.md) for more diagnostics, or [GITHUB_ISSUE.md](https://raw.githubusercontent.com/mims-harvard/ToolUnive
skills/setup-tooluniverse/TROUBLESHOOTING.md
- Dangerous shell operationline 12
# or: sudo apt install python3.12 # Ubuntu/Debian - Dangerous shell operationline 22
curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 22
curl -LsSf https://astral.sh/uv/install.sh | sh - Dangerous shell operationline 64
sudo ln -sf "$(which uvx)" /usr/local/bin/uvx # Intel Mac / Linux - Dangerous shell operationline 66
sudo ln -sf "$(which uvx)" /opt/homebrew/bin/uvx - Access to secretsline 100
[Credential-related source omitted] - Access to secretsline 106
[Credential-related source omitted] - Access to secretsline 108
[Credential-related source omitted] - Network accessline 183
# In your MCP config args: "args": ["tooluniverse==<last-known-good-version>"] # Check releases: https://github.com/mims-harvard/ToolUniverse/releases - Network accessline 185
# Check releases: https://github.com/mims-harvard/ToolUniverse/releases - Network accessline 224
body = "\n".join(lines) title = "Bug: <brief description>" url = ("https://github.com/mims-harvard/ToolUniverse/issues/new" - Network accessline 225
title = "Bug: <brief description>" url = ("https://github.com/mims-harvard/ToolUniverse/issues/new" "?title=" + urllib.parse.quote(title) - Network accessline 226
url = ("https://github.com/mims-harvard/ToolUniverse/issues/new" "?title=" + urllib.parse.quote(title) + "&body=" + urllib.parse.quote(body)) - Network accessline 242
The script prints two things: 1. **Issue body** — copy-paste it into https://github.com/mims-harvard/ToolUniverse/issues/new - Network accessline 243
1. **Issue body** — copy-paste it into https://github.com/mims-harvard/ToolUniverse/issues/new
skills/setup-tooluniverse/scripts/check_prerequisites.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 78
else: print(" ⚠<U+FE0F> NOT FOUND: uv not installed (recommended for MCP)") print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh") - Network accessline 79
print(" ⚠<U+FE0F> NOT FOUND: uv not installed (recommended for MCP)") print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh") return False - Dangerous shell operationline 80
print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh") - Network accessline 80
print(" Install with: curl -LsSf https://astral.sh/uv/install.sh | sh")
skills/setup-tooluniverse/scripts/diagnose_setup.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/setup-tooluniverse/scripts/list_tool_categories.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/setup-tooluniverse/scripts/test_mcp_connection.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/setup-tooluniverse/scripts/verify_installation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/setup-transcriptformer-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/transcriptformer > curl -fsSL -o ~/.tooluniverse/envs/transcriptformer-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/transcriptformer > curl -fsSL -o ~/.tooluniverse/envs/transcriptformer-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > curl -fsSL -o ~/.tooluniverse/env - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/transcriptformer-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 13
> curl -fsSL -o ~/.tooluniverse/envs/transcriptformer-requirements.txt https://raw.githubusercontent.com/mims-harvard/ToolUniverse/main/src/tooluniverse/remote/transcriptformer/requirements.txt - Network accessline 14
> ~/.local/bin/uv pip install --python ~/.tooluniverse/envs/transcriptformer/bin/python -r ~/.tooluniverse/envs/transcriptformer-tooluniverse.txt -r ~/.tooluniverse/envs/transcriptformer-requirements.txt > ~/.local/bin/tu remote check trans - Network accessline 15
> ~/.local/bin/tu remote check transcriptformer --python ~/.tooluniverse/envs/transcriptformer/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow transcriptformer --python ~/.tooluniverse/envs/transcriptformer/bin/p - Network accessline 16
> ~/.local/bin/tu serve --allow transcriptformer --python ~/.tooluniverse/envs/transcriptformer/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:7000/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:7000/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:7000/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:7000/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:7000/mcp --name validation-transcriptformer --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"state":"control","cell_type":"b_cell","gene_names":["BRAF"],"disease":"breast_cancer"}''') async with Client("http://127.0.0.1:7000/mcp") as client: - Network accessline 162
arguments = json.loads('''{"state":"control","cell_type":"b_cell","gene_names":["BRAF"],"disease":"breast_cancer"}''') async with Client("http://127.0.0.1:7000/mcp") as client: result = await client.call_tool("run_transcriptform - Network accessline 163
async with Client("http://127.0.0.1:7000/mcp") as client:
skills/setup-uspto-downloader-remote-tool/SKILL.md
- Network accessline 10
> ```bash > ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/uspto-downloader > curl -fsSL -o ~/.tooluniverse/envs/uspto-downloader-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 11
> ~/.local/bin/uv venv --python 3.12 ~/.tooluniverse/envs/uspto-downloader > curl -fsSL -o ~/.tooluniverse/envs/uspto-downloader-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt > ~/.local/bin/uv pip install --pyt - Network accessline 12
> curl -fsSL -o ~/.tooluniverse/envs/uspto-downloader-tooluniverse.txt https://connect.aiscientist.tools/install-requirements.txt - Network accessline 14
> # then this model's own dependencies, as in "Create an isolated environment" below > ~/.local/bin/tu remote check uspto-downloader --python ~/.tooluniverse/envs/uspto-downloader/bin/python # lists anything still missing > ~/.local/bin/t - Network accessline 15
> ~/.local/bin/tu remote check uspto-downloader --python ~/.tooluniverse/envs/uspto-downloader/bin/python # lists anything still missing > ~/.local/bin/tu serve --allow uspto-downloader --python ~/.tooluniverse/envs/uspto-downloader/bin/p - Network accessline 16
> ~/.local/bin/tu serve --allow uspto-downloader --python ~/.tooluniverse/envs/uspto-downloader/bin/python --share --detach --name "<computer name>" --service https://tooluniverse-backend.onrender.com - Access to secretsline 57
[Credential-related source omitted] - Network accessline 110
The Streamable HTTP endpoint is http://127.0.0.1:8081/mcp. In a second activated shell run: - Network accessline 117
async def main(): async with Client("http://127.0.0.1:8081/mcp") as client: - Network accessline 118
async with Client("http://127.0.0.1:8081/mcp") as client: - Network accessline 132
python -m pip install fastmcp pyyaml "tuplatform-connect @ https://connect.aiscientist.tools/downloads/tuplatform_connect-0.3.0-py3-none-any.whl#sha256=3fad5eee5ecf7887a693d93ccd1aa112dc0955617a885d1fc3daded0030f9ae0" - Network accessline 133
tu doctor --forward http://127.0.0.1:8081/mcp --json - Network accessline 134
tu serve --share --forward http://127.0.0.1:8081/mcp --name validation-uspto-downloader --workers 1 - Network accessline 161
async def main(): arguments = json.loads('''{"applicationNumberText":"12345678"}''') async with Client("http://127.0.0.1:8081/mcp") as client: - Network accessline 162
arguments = json.loads('''{"applicationNumberText":"12345678"}''') async with Client("http://127.0.0.1:8081/mcp") as client: result = await client.call_tool("get_abstract_from_patent_app_number", arguments) - Network accessline 163
async with Client("http://127.0.0.1:8081/mcp") as client:
skills/tooluniverse-acmg-variant-classification/SKILL.md
- Access to secretsline 112
[Credential-related source omitted]
skills/tooluniverse-adverse-event-detection/test_adverse_event_detection.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-antibody-engineering/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
skills/tooluniverse-antibody-engineering/scripts/developability.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-antigravity-plugin/SKILL.md
- Dangerous shell operationline 14
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 14
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 15
agy --version # Antigravity CLI; if missing: https://antigravity.google - Network accessline 32
git clone https://github.com/mims-harvard/ToolUniverse.git - Access to secretsline 87
[Credential-related source omitted] - Access to secretsline 88
[Credential-related source omitted] - Access to secretsline 89
[Credential-related source omitted] - Network accessline 109
Still stuck: https://github.com/mims-harvard/ToolUniverse/issues
skills/tooluniverse-binder-discovery/CHECKLIST.md
- Access to secretsline 23
[Credential-related source omitted]
skills/tooluniverse-binder-discovery/EXAMPLES.md
- Access to secretsline 385
[Credential-related source omitted] - Network accessline 456
# (no dedicated PDB-download tool; fetch the file from RCSB) pdb_content = tu.tools.download_text_content( url="https://files.rcsb.org/download/2W9Z.pdb" - Network accessline 457
pdb_content = tu.tools.download_text_content( url="https://files.rcsb.org/download/2W9Z.pdb" )['content'] - Network accessline 458
url="https://files.rcsb.org/download/2W9Z.pdb" - Access to secretsline 711
[Credential-related source omitted] - Access to secretsline 715
[Credential-related source omitted]
skills/tooluniverse-binder-discovery/SKILL.md
- Access to secretsline 92
[Credential-related source omitted] - Access to secretsline 93
[Credential-related source omitted] - Access to secretsline 129
[Credential-related source omitted] - Access to secretsline 142
[Credential-related source omitted] - Access to secretsline 167
[Credential-related source omitted] - Network accessline 258
# PubChem batch property retrieval (up to 100 CIDs per call) cids = "2244,5988,3672" url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" - Network accessline 259
cids = "2244,5988,3672" url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" props = pd.DataFrame(requests.get(url).json()["PropertyTable"]["Prope - Network accessline 260
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA,HBondDonorCount,HBondAcceptorCount/JSON" - Network accessline 261
props = pd.DataFrame(requests.get(url).json()["PropertyTable"]["Properties"]) - Network accessline 263
# ChEMBL bioactivity bulk download for a target target_id = "CHEMBL203" # EGFR url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" - Network accessline 264
target_id = "CHEMBL203" # EGFR url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" activities = requests.get(url).json()["activities"] - Network accessline 265
url = f"https://www.ebi.ac.uk/chembl/api/data/activity.json?target_chembl_id={target_id}&pchembl_value__gte=5&limit=1000" activities = requests.get(url).json()["activities"] df = pd.DataFrame(activities)[["molecule_chembl_id", "canonical_sm - Network accessline 266
activities = requests.get(url).json()["activities"] - Network accessline 273
# SDF download from PubChem (for docking input) sdf_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/SDF" - Network accessline 274
sdf_url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/SDF" - Network accessline 275
sdf_content = requests.get(sdf_url).text - Access to secretsline 286
[Credential-related source omitted]
skills/tooluniverse-binder-discovery/TOOLS_REFERENCE.md
- Access to secretsline 214
[Credential-related source omitted] - Access to secretsline 254
[Credential-related source omitted] - Access to secretsline 472
[Credential-related source omitted] - Access to secretsline 580
[Credential-related source omitted] - Access to secretsline 703
[Credential-related source omitted] - Access to secretsline 972
[Credential-related source omitted] - Access to secretsline 973
[Credential-related source omitted]
skills/tooluniverse-binder-discovery/WORKFLOW_DETAILS.md
skills/tooluniverse-bioinformatics-resource-discovery/SKILL.md
- Network accessline 48
Verified live: `SmartAPI_search_apis {"query": "biothings", "limit": 3}` -> real APIs including "Biothings Therapeutic Target Database API" (`base_url: https://biothings.transltr.io/ttd`) and "MyGeneset.info API". Feeding that `api_id` into
skills/tooluniverse-biomedical-fact-lookup/SKILL.md
skills/tooluniverse-biomedical-fact-lookup/test_snippets.py
- Executable scriptline 1
Executable file - Dangerous shell operationline 17
exec(b, ns) # define functions - Network accessline 45
assert n0 == 1, "uncut linear -> 1 fragment" nc, _ = digest("AAAAGAATTCAAAA", ["EcoRI"], circular=True) assert nc == 1, "one cut on a circle -> 1 fragment" - Network accessline 46
nc, _ = digest("AAAAGAATTCAAAA", ["EcoRI"], circular=True) assert nc == 1, "one cut on a circle -> 1 fragment" print("digest OK linear1:", n1, "linear2:", n2, "uncut:", n0, "circular1:", nc) - Network accessline 47
assert nc == 1, "one cut on a circle -> 1 fragment"
skills/tooluniverse-cancer-genomics-tcga/SKILL.md
- Network accessline 302
offset = 0 while True: resp = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 303
while True: resp = requests.post("https://api.gdc.cancer.gov/cases", json={ "filters": filters, "size": 500, "from": offset, - Network accessline 304
resp = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 314
# Download MAF mutation file by UUID file_uuid = "abc123-..." # from GDC_list_files result url = f"https://api.gdc.cancer.gov/data/{file_uuid}" - Network accessline 315
file_uuid = "abc123-..." # from GDC_list_files result url = f"https://api.gdc.cancer.gov/data/{file_uuid}" content = requests.get(url, headers={"Content-Type": "application/json"}).content - Network accessline 316
url = f"https://api.gdc.cancer.gov/data/{file_uuid}" - Network accessline 317
content = requests.get(url, headers={"Content-Type": "application/json"}).content
skills/tooluniverse-cancer-variant-interpretation/SKILL.md
- Access to secretsline 124
[Credential-related source omitted]
skills/tooluniverse-cancer-variant-interpretation/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-cell-line-profiling/SKILL.md
- Network accessline 137
**Per-cell-line Chronos scores** (what the API can't give you): use the bundled script `scripts/depmap_gene_dependency.py`. It pulls the current DepMap Public release (CRISPRGeneEffect.csv + Model.csv) once via the public download index, ca
skills/tooluniverse-cell-line-profiling/scripts/depmap_gene_dependency.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 37
DEPMAP_INDEX = "https://depmap.org/portal/api/download/files" - Access to secretsline 38
[Credential-related source omitted] - Network accessline 49
with urllib.request.urlopen(DEPMAP_INDEX, timeout=60) as fh: - Network accessline 55
f"DepMap's download index ({DEPMAP_INDEX}) returned a web page instead of " f"the file list, so {filename} cannot be fetched automatically. Download " f"CRISPRGeneEffect.csv and Model.csv from https://depmap.org/port - Network accessline 56
f"the file list, so {filename} cannot be fetched automatically. Download " f"CRISPRGeneEffect.csv and Model.csv from https://depmap.org/portal/download/all/ " f"in a browser, put them in {CACHE_DIR} (or set DEPMAP_CA - Network accessline 57
f"CRISPRGeneEffect.csv and Model.csv from https://depmap.org/portal/download/all/ " - Network accessline 77
if not os.path.exists(path) or os.path.getsize(path) == 0: sys.stderr.write(f"Downloading {filename} (one-time; CRISPRGeneEffect is large)...\n") urllib.request.urlretrieve(_latest_url(filename), path) - Network accessline 78
sys.stderr.write(f"Downloading {filename} (one-time; CRISPRGeneEffect is large)...\n") urllib.request.urlretrieve(_latest_url(filename), path) return path - Network accessline 79
urllib.request.urlretrieve(_latest_url(filename), path)
skills/tooluniverse-chemical-safety/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-claude-code-plugin/SKILL.md
- Dangerous shell operationline 14
uv --version # must exist; if not: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 14
uv --version # must exist; if not: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 15
claude --version # Claude Code CLI; if not: https://claude.com/claude-code - Dangerous shell operationline 62
rm -rf ~/.claude/skills/tooluniverse-* - Dangerous shell operationline 63
rm -rf ~/.claude/skills/create-tooluniverse-skill - Dangerous shell operationline 64
rm -rf ~/.claude/skills/setup-tooluniverse - Network accessline 75
Replace `1.2.2` with any released version from https://pypi.org/project/tooluniverse/. Restart Claude Code to apply. (`.mcp.json` location is shown under "API keys" below.) - Access to secretsline 131
[Credential-related source omitted] - Access to secretsline 132
[Credential-related source omitted] - Network accessline 168
git clone https://github.com/mims-harvard/ToolUniverse.git - Network accessline 176
Grab `tooluniverse-plugin-vX.Y.Z.zip` from https://github.com/mims-harvard/ToolUniverse/releases, unzip, then: - Network accessline 195
Still stuck: https://github.com/mims-harvard/ToolUniverse/issues - Network accessline 205
# 1. Is uv/uvx installed? (most common cause of "no tools at all") command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Dangerous shell operationline 206
command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Network accessline 206
command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Dangerous shell operationline 223
ls ~/.claude/skills/tooluniverse-* 2>/dev/null && echo "FIX: rm -rf ~/.claude/skills/tooluniverse-*"
skills/tooluniverse-clinical-guidelines/SKILL.md
- Access to secretsline 124
[Credential-related source omitted]
skills/tooluniverse-clinical-guidelines/TOOLS_REFERENCE.md
- Access to secretsline 52
[Credential-related source omitted] - Network accessline 196
- **Returns**: dict with patient guideline text - **⚠<U+FE0F> CRITICAL**: Pass full URL, NOT integer ID - **Example**: `NCCN_get_patient_guideline(url='https://www.nccn.org/patientresources/patient-resources/guidelines-for-patients/guidelines-for- - Network accessline 198
- **Example**: `NCCN_get_patient_guideline(url='https://www.nccn.org/patientresources/patient-resources/guidelines-for-patients/guidelines-for-patients-details?patientGuidelineId=61')`
skills/tooluniverse-clinical-terminology-lookup/SKILL.md
- Access to secretsline 124
[Credential-related source omitted] - Network accessline 422
"22298006"}` -> `"Myocardial infarction"`. The standout use is subsumption expansion: `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006"}` returns every clinical - Network accessline 423
subsumption expansion: `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006"}` returns every clinical subtype (verified live: "Old myocardial infarction", "Acute myocardial - Network accessline 424
"http://snomed.info/sct?fhir_vs=isa/22298006"}` returns every clinical
skills/tooluniverse-clinical-terminology-lookup/references/clinical_tables_tool_reference.md
- Network accessline 202
"error", "error": "ICD API authentication required. Set ICD_CLIENT_ID and ICD_CLIENT_SECRET environment variables. Register at: https://icd.who.int/icdapi"}`. Same for `ICD11_get_entity` (2/2 failed) - Network accessline 203
[Credential-related source omitted] - Access to secretsline 203
[Credential-related source omitted] - Network accessline 204
https://icd.who.int/icdapi"}`. Same for `ICD11_get_entity` (2/2 failed) - Network accessline 214
for a user is the same either way (a clear error, no fabricated data), just via a different code path than the more common gating pattern. Register free at https://icd.who.int/icdapi to use these. - Network accessline 216
Register free at https://icd.who.int/icdapi to use these. - Network accessline 361
All 4 tools type `BioPortalTool`, no API key required. `get_concept`/ `get_hierarchy` need a full IRI (`http://purl.obolibrary.org/obo/DOID_1909` - Network accessline 362
`get_hierarchy` need a full IRI (`http://purl.obolibrary.org/obo/DOID_1909` - Network accessline 380
{"matched_text": "HYPERTENSION", "from": 23, "to": 34, "match_type": "PREF", "concept_label": "hypertension", "concept_id": "DOID_10763", "concept_full_id": "http://purl.obolibrary.org/obo/DOID_10763"}, - Network accessline 381
"concept_label": "hypertension", "concept_id": "DOID_10763", "concept_full_id": "http://purl.obolibrary.org/obo/DOID_10763"}, {"matched_text": "TYPE 2 DIABETES MELLITUS", "from": 40, "to": 63, "match_type": "PREF", - Network accessline 382
"concept_full_id": "http://purl.obolibrary.org/obo/DOID_10763"}, - Network accessline 385
{"matched_text": "METFORMIN", "from": 77, "to": 85, "match_type": "PREF", "concept_label": "metformin", "concept_id": "6809", "concept_full_id": "http://purl.bioontology.org/ontology/RXNORM/6809"} - Network accessline 386
"concept_label": "metformin", "concept_id": "6809", "concept_full_id": "http://purl.bioontology.org/ontology/RXNORM/6809"} ], "metadata": {"source": "BioPortal Annotator (NCBO)", "total_annotations": 3, "text_length": 86}} - Network accessline 387
"concept_full_id": "http://purl.bioontology.org/ontology/RXNORM/6809"} - Network accessline 404
or a full FHIR system URI), `code` (required). `tu test` PASS, 2/2: `{"system": "snomed", "code": "22298006"}` -> `{"code": "22298006", "display": "Myocardial infarction", "system": "http://snomed.info/sct"}`; - Network accessline 405
`{"system": "snomed", "code": "22298006"}` -> `{"code": "22298006", "display": "Myocardial infarction", "system": "http://snomed.info/sct"}`; `{"system": "snomed", "code": "73211009"}` -> `"Diabetes mellitus"`. - Network accessline 406
"display": "Myocardial infarction", "system": "http://snomed.info/sct"}`; - Network accessline 408
- **`FHIRTerminology_expand_valueset`** — `value_set_url` (required), `limit` (int, 1-500, default 50). `tu test` PASS, 2/2. **Custom call** `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006", "limit": 5}` - Network accessline 409
`limit` (int, 1-500, default 50). `tu test` PASS, 2/2. **Custom call** `{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006", "limit": 5}` -> real subtype list: `1755008` ("Old myocardial infarction"), - Network accessline 410
`{"value_set_url": "http://snomed.info/sct?fhir_vs=isa/22298006", "limit": 5}` - Network accessline 411
-> real subtype list: `1755008` ("Old myocardial infarction"), `15990001` ("Acute myocardial infarction of posterolateral wall"), plus more, all sharing `"system": "http://snomed.info/sct"`. Use the - Network accessline 412
`15990001` ("Acute myocardial infarction of posterolateral wall"), plus more, all sharing `"system": "http://snomed.info/sct"`. Use the `isa/<code>` value-set URL pattern for "every descendant of this SNOMED - Network accessline 413
more, all sharing `"system": "http://snomed.info/sct"`. Use the
skills/tooluniverse-clinical-trial-design/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
skills/tooluniverse-clinical-trial-design/SKILL.md
- Network accessline 233
[Credential-related source omitted] - Network accessline 234
[Credential-related source omitted] - Network accessline 235
resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json() - Network accessline 252
# FDA drug approval history drug = "pembrolizumab" fda = requests.get(f"https://api.fda.gov/drug/drugsfda.json?search=openfda.brand_name:{drug}&limit=10").json() - Network accessline 254
fda = requests.get(f"https://api.fda.gov/drug/drugsfda.json?search=openfda.brand_name:{drug}&limit=10").json()
skills/tooluniverse-clinical-trial-design/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-clinical-trial-design/trial_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-clinical-trial-matching/QUICK_START.md
- Network accessline 136
- Trial availability changes frequently; always verify current status at [ClinicalTrials.gov](https://clinicaltrials.gov)
skills/tooluniverse-clinical-trial-matching/REPORT_TEMPLATE.md
- Network accessline 178
This report is for informational and research purposes only. Clinical trial eligibility is ultimately determined by the trial investigators based on complete medical records. Patients should discuss all options with their healthcare team. T
skills/tooluniverse-clinical-trial-matching/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-clinical-trial-protocol/scripts/sample_size_calculator.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-codex-plugin/SKILL.md
- Dangerous shell operationline 13
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 13
uv --version # provides `uvx`; if missing: curl -LsSf https://astral.sh/uv/install.sh | sh - Network accessline 14
codex --version # OpenAI Codex CLI; if missing: https://developers.openai.com/codex - Access to secretsline 61
[Credential-related source omitted] - Access to secretsline 62
[Credential-related source omitted] - Access to secretsline 63
[Credential-related source omitted] - Network accessline 98
Replace `1.3.0` with any release from https://pypi.org/project/tooluniverse/, then restart Codex. (Find the file under `~/.codex/plugins/cache/`.) - Network accessline 110
git clone https://github.com/mims-harvard/ToolUniverse.git - Network accessline 130
Still stuck: https://github.com/mims-harvard/ToolUniverse/issues - Network accessline 137
# 1. Is uv/uvx installed? (most common cause of "no tools at all") command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Dangerous shell operationline 138
command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh" - Network accessline 138
command -v uvx || echo "FIX: curl -LsSf https://astral.sh/uv/install.sh | sh"
skills/tooluniverse-comparative-genomics/scripts/dnds.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-computational-biophysics/scripts/burn_fluids.py
- Executable scriptline 1
Executable file
skills/tooluniverse-computational-biophysics/scripts/env_risk_assessment.py
- Executable scriptline 1
Executable file
skills/tooluniverse-computational-biophysics/scripts/enzyme_kinetics.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-computational-biophysics/scripts/epidemiology.py
- Executable scriptline 1
Executable file
skills/tooluniverse-computational-biophysics/scripts/fluid_calculations.py
- Executable scriptline 1
Executable file
skills/tooluniverse-computational-biophysics/scripts/herd_immunity.py
- Executable scriptline 1
Executable file
skills/tooluniverse-computational-biophysics/scripts/iv_drip_rate.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-computational-biophysics/scripts/mc_analyzer.py
- Executable scriptline 1
Executable file
skills/tooluniverse-computational-biophysics/scripts/radioactive_decay.py
skills/tooluniverse-connect/SKILL.md
- Network accessline 14
| What you see | The user is on | |---|---| | ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which - Network accessline 15
|---|---| | ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which still works) | **ToolUniverse Con - Network accessline 16
| ToolUniverse tools such as `what_can_i_use`, `list_skills` or `get_skill`, from the server `https://mcp.aiscientist.tools` (earlier setups: `https://tooluniverse-backend.onrender.com/mcp`, which still works) | **ToolUniverse Connect** (ho - Network accessline 36
The user needs a free account: https://connect.aiscientist.tools/register. Its **Your AI scientist** page (https://connect.aiscientist.tools/connect-agent) shows these steps for each app. - Network accessline 40
- **Claude (claude.ai or the desktop app):** **Customize → Connectors**, click **+ Add**, then **Add custom connector**. Name it ToolUniverse, URL `https://mcp.aiscientist.tools`. Sign in and click **Allow**. In each new chat, click **+** u - Network accessline 41
- **Claude Code:** run `claude mcp add --transport http --scope user tooluniverse "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- claude-code && claude mcp login tooluniverse` A brows - Dangerous shell operationline 42
`claude mcp add --transport http --scope user tooluniverse "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- claude-code && claude mcp login tooluniverse` - Network accessline 42
`claude mcp add --transport http --scope user tooluniverse "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- claude-code && claude mcp login tooluniverse` - Network accessline 43
A browser page opens; the user signs in and clicks **Allow**. Then start a new Claude Code session. - **Codex:** run `codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills. - Network accessline 44
- **Codex:** run `codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- codex` It starts the browser sign-in; if no page opens, run `codex mcp login tooluni - Dangerous shell operationline 45
`codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- codex` - Network accessline 45
`codex mcp add tooluniverse --url "https://mcp.aiscientist.tools" && curl -fsSL https://connect.aiscientist.tools/skills.sh | sh -s -- codex` - Network accessline 58
[Credential-related source omitted] - Access to secretsline 58
[Credential-related source omitted] - Access to secretsline 59
[Credential-related source omitted] - Network accessline 77
- **No ToolUniverse tools in this chat:** the app is not connected, or in Claude the connector is off for this chat. Follow section 3. - **Asked to sign in again, or "unauthorized":** Claude Code: `claude mcp login tooluniverse`. Codex: `co - Network accessline 78
- **Asked to sign in again, or "unauthorized":** Claude Code: `claude mcp login tooluniverse`. Codex: `codex mcp login tooluniverse`. Claude: **Customize → Connectors**, open ToolUniverse and connect again. A password reset signs out every - Network accessline 79
- **"Starting" or "unavailable" right after an update:** the service is restarting. Wait a minute and try again. Live status: https://connect.aiscientist.tools/status. - Network accessline 84
Privacy, in one line if asked: the user's conversation stays with their AI app's provider; Connect receives only the tool requests the AI app sends. Details: https://connect.aiscientist.tools/privacy.
skills/tooluniverse-crispr-screen-analysis/scripts/reference_gene_sets.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-cs-setup/kernel.py
- Executable scriptline 1
Executable file - Network accessline 16
REPO_TARBALL = "https://codeload.github.com/mims-harvard/ToolUniverse/tar.gz/refs/heads/" - Network accessline 53
tgz = os.path.join(staging, "repo.tar.gz") urllib.request.urlretrieve(REPO_TARBALL + ref, tgz) - Network accessline 54
urllib.request.urlretrieve(REPO_TARBALL + ref, tgz)
skills/tooluniverse-cs-setup/templates/research_kernel.py
skills/tooluniverse-custom-tool/SKILL.md
- Network accessline 41
"type": "BaseRESTTool", "fields": { "endpoint": "https://my-api.example.com/search" - Network accessline 42
"fields": { "endpoint": "https://my-api.example.com/search" }, - Network accessline 43
"endpoint": "https://my-api.example.com/search" - Network accessline 87
def run(self, q: str, limit: int = 10) -> dict: import requests resp = requests.get( - Network accessline 88
import requests resp = requests.get( "https://my-api.example.com/search", - Network accessline 89
resp = requests.get( - Network accessline 90
"https://my-api.example.com/search", - Network accessline 188
return {"error": "query parameter is required"} try: resp = requests.get( - Network accessline 189
try: resp = requests.get( "https://my-api.example.com/search", - Network accessline 190
resp = requests.get( - Network accessline 191
"https://my-api.example.com/search",
skills/tooluniverse-custom-tool/references/json-tool.md
- Network accessline 9
"description": "...", "type": "BaseRESTTool", "fields": { "endpoint": "https://api.example.com/search" }, - Network accessline 10
"type": "BaseRESTTool", "fields": { "endpoint": "https://api.example.com/search" }, "parameter": { - Network accessline 11
"fields": { "endpoint": "https://api.example.com/search" }, - Network accessline 71
"type": "BaseRESTTool", "fields": { "endpoint": "https://api.example.com/jobs", - Network accessline 72
"fields": { "endpoint": "https://api.example.com/jobs", "method": "POST", - Network accessline 73
"endpoint": "https://api.example.com/jobs", - Network accessline 97
"fields": { "endpoint": "https://api.example.com/search", - Network accessline 98
"endpoint": "https://api.example.com/search", - Access to secretsline 99
[Credential-related source omitted] - Access to secretsline 111
[Credential-related source omitted] - Access to secretsline 113
[Credential-related source omitted] - Network accessline 114
"domain": "Drugs & Chemistry", "type": "secret", "register_url": "https://example.com/get-a-key", - Network accessline 115
"type": "secret", "register_url": "https://example.com/get-a-key", "purpose": "What this key unlocks (one sentence).", - Network accessline 116
"register_url": "https://example.com/get-a-key", - Access to secretsline 135
[Credential-related source omitted] - Network accessline 215
import urllib.request, json with urllib.request.urlopen("https://api.example.com/search?q=test") as r: - Network accessline 216
with urllib.request.urlopen("https://api.example.com/search?q=test") as r: - Network accessline 225
**Verify the URL is a real JSON endpoint.** Some URLs that look like REST APIs (e.g., `https://certification.example.org/api/projects`) may redirect to a static HTML page. A urllib fetch will show you the Content-Type and body immediately, - Network accessline 226
(e.g., `https://certification.example.org/api/projects`) may redirect to a static HTML page.
skills/tooluniverse-custom-tool/references/python-tool.md
- Network accessline 27
def run(self, q: str, limit: int = 10) -> dict: import requests resp = requests.get( - Network accessline 28
import requests resp = requests.get( "https://my-api.example.com/search", - Network accessline 29
resp = requests.get( - Network accessline 30
"https://my-api.example.com/search", - Network accessline 68
return {"error": "query parameter is required"} try: resp = requests.get( - Network accessline 69
try: resp = requests.get( "https://my-api.example.com/search", - Network accessline 70
resp = requests.get( - Network accessline 71
"https://my-api.example.com/search", - Network accessline 223
if _cache is None: import requests resp = requests.get("https://api.example.com/full-index", timeout=timeout) - Network accessline 224
import requests resp = requests.get("https://api.example.com/full-index", timeout=timeout) resp.raise_for_status() - Network accessline 225
resp = requests.get("https://api.example.com/full-index", timeout=timeout) - Access to secretsline 248
[Credential-related source omitted] - Access to secretsline 249
[Credential-related source omitted] - Access to secretsline 250
[Credential-related source omitted] - Network accessline 299
import requests try: resp = requests.get("https://api.example.com/search", params={"q": q}, timeout=30) - Network accessline 300
try: resp = requests.get("https://api.example.com/search", params={"q": q}, timeout=30) resp.raise_for_status() - Network accessline 301
resp = requests.get("https://api.example.com/search", params={"q": q}, timeout=30)
skills/tooluniverse-data-wrangling/SKILL.md
- Network accessline 62
import pysam # BAM (requires pysam) bam = pysam.AlignmentFile("aligned.bam", "rb") for read in bam.fetch("chr1", 1000, 2000): print(read.query_name) - Network accessline 64
for read in bam.fetch("chr1", 1000, 2000): print(read.query_name) - Network accessline 164
import requests base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" - Network accessline 165
base = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" # Search -> get IDs -> fetch records in batches ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA1+AND+human&retmax=500&retmode=json").json() - Network accessline 166
# Search -> get IDs -> fetch records in batches ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA1+AND+human&retmax=500&retmode=json").json() id_list = ids["esearchresult"]["idlist"] - Network accessline 167
ids = requests.get(f"{base}/esearch.fcgi?db=gene&term=BRCA1+AND+human&retmax=500&retmode=json").json() - Network accessline 170
for i in range(0, len(id_list), 500): batch = ",".join(id_list[i:i+500]) data = requests.get(f"{base}/efetch.fcgi?db=gene&id={batch}&retmode=xml").text - Network accessline 172
data = requests.get(f"{base}/efetch.fcgi?db=gene&id={batch}&retmode=xml").text - Network accessline 178
# UniProt bulk TSV download with cursor pagination url = "https://rest.uniprot.org/uniprotkb/search?query=organism_id:9606+AND+keyword:kinase&format=tsv&size=500" - Network accessline 179
url = "https://rest.uniprot.org/uniprotkb/search?query=organism_id:9606+AND+keyword:kinase&format=tsv&size=500" - Network accessline 180
all_rows = [] while url: resp = requests.get(url) - Network accessline 181
while url: resp = requests.get(url) all_rows.append(resp.text) - Network accessline 182
resp = requests.get(url) - Network accessline 193
{"op":"=","content":{"field":"demographic.vital_status","value":"Dead"}} ]} cases = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 194
]} cases = requests.post("https://api.gdc.cancer.gov/cases", json={ "filters": filters, "fields": "demographic.vital_status,diagnoses.days_to_death", - Network accessline 195
cases = requests.post("https://api.gdc.cancer.gov/cases", json={ - Network accessline 204
# Direct NHANES XPT download (any cycle, any component) cycle, component = "2017-2018", "DEMO_J" url = f"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/{component}.XPT" - Network accessline 205
cycle, component = "2017-2018", "DEMO_J" url = f"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/{component}.XPT" df = pd.read_sas(io.BytesIO(requests.get(url).content), format="xport") - Network accessline 206
url = f"https://wwwn.cdc.gov/Nchs/Data/Nhanes/Public/2017/DataFiles/{component}.XPT" - Network accessline 207
df = pd.read_sas(io.BytesIO(requests.get(url).content), format="xport") - Network accessline 213
# GWAS Catalog full download (37MB TSV, all associations) url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 214
url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 223
# PubChem batch property retrieval (up to 100 CIDs at once) cids = "2244,5988,3672" # aspirin, sucrose, ibuprofen url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA/JSON" - Network accessline 224
cids = "2244,5988,3672" # aspirin, sucrose, ibuprofen url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA/JSON" props = requests.get(url).json()["PropertyTable"]["Properties"] - Network accessline 225
url = f"https://pubchem.ncbi.nlm.nih.gov/rest/pug/compound/cid/{cids}/property/MolecularWeight,XLogP,TPSA/JSON" - Network accessline 226
props = requests.get(url).json()["PropertyTable"]["Properties"] - Network accessline 232
# GEO series matrix direct download geo_id = "GSE12345" url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{geo_id[:5]}nnn/{geo_id}/matrix/{geo_id}_series_matrix.txt.gz" - Network accessline 233
geo_id = "GSE12345" url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{geo_id[:5]}nnn/{geo_id}/matrix/{geo_id}_series_matrix.txt.gz" df = pd.read_csv(url, sep="\t", comment="!", index_col=0) - Network accessline 234
url = f"https://ftp.ncbi.nlm.nih.gov/geo/series/{geo_id[:5]}nnn/{geo_id}/matrix/{geo_id}_series_matrix.txt.gz" - Network accessline 237
# GTEx bulk expression (median TPM per tissue) url = "https://storage.googleapis.com/adult-gtex/bulk-gex/v8/rna-seq/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.gz" - Network accessline 238
url = "https://storage.googleapis.com/adult-gtex/bulk-gex/v8/rna-seq/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_median_tpm.gct.gz" - Network accessline 249
[Credential-related source omitted] - Network accessline 250
[Credential-related source omitted] - Network accessline 251
resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params).json() - Network accessline 262
all_results = [] while cursor: resp = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 263
while cursor: resp = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", params={"query": "BRCA1 AND resistance", "format": "json", "pageSize": 100, "cursorMark": cursor}).json() - Network accessline 264
resp = requests.get("https://www.ebi.ac.uk/europepmc/webservices/rest/search", - Network accessline 273
# Zenodo: search + download files record = requests.get("https://zenodo.org/api/records", params={"q": "proteomics cancer", "size": 5}).json()["hits"]["hits"][0] - Network accessline 274
record = requests.get("https://zenodo.org/api/records", params={"q": "proteomics cancer", "size": 5}).json()["hits"]["hits"][0] - Network accessline 276
content = requests.get(f["links"]["self"]).content # download each file - Network accessline 335
offset = 0 while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30).json() - Network accessline 336
while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30).json() batch = resp.get("data", resp.get("results", resp.get("hits", []))) - Network accessline 337
resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30).json() - Network accessline 346
[Credential-related source omitted] - Network accessline 347
[Credential-related source omitted] - Network accessline 348
resp = requests.get(api_url, params=params).json() - Network accessline 357
def fetch_with_retry(url, max_retries=3, **kwargs): for attempt in range(max_retries): resp = requests.get(url, timeout=30, **kwargs) - Network accessline 358
for attempt in range(max_retries): resp = requests.get(url, timeout=30, **kwargs) if resp.status_code == 200: return resp - Network accessline 359
resp = requests.get(url, timeout=30, **kwargs) - Access to secretsline 373
[Credential-related source omitted] - Access to secretsline 375
[Credential-related source omitted] - Network accessline 381
def download_large_file(url, output_path): with requests.get(url, stream=True, timeout=300) as r: - Network accessline 382
with requests.get(url, stream=True, timeout=300) as r: - Network accessline 391
resp = requests.get(url, timeout=30)
skills/tooluniverse-data-wrangling/references/specialized-domains.md
- Network accessline 26
# PRIDE project files — search then download raw/processed data project = requests.get("https://www.ebi.ac.uk/pride/ws/archive/v2/projects/PXD012345").json() - Network accessline 27
project = requests.get("https://www.ebi.ac.uk/pride/ws/archive/v2/projects/PXD012345").json() - Network accessline 28
files = requests.get(f"https://www.ebi.ac.uk/pride/ws/archive/v2/projects/PXD012345/files").json() - Network accessline 33
# ProteomeXchange: search across PRIDE + MassIVE + jPOST px = requests.get("https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD012345&outputMode=JSON").json() - Network accessline 34
px = requests.get("https://proteomecentral.proteomexchange.org/cgi/GetDataset?ID=PXD012345&outputMode=JSON").json() - Network accessline 40
# MetaboLights study download study_id = "MTBLS1234" study = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}").json() - Network accessline 41
study_id = "MTBLS1234" study = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}").json() # Download metabolite assignment file - Network accessline 42
study = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}").json() - Network accessline 44
files = requests.get(f"https://www.ebi.ac.uk/metabolights/ws/studies/{study_id}/files").json() - Network accessline 46
# Metabolomics Workbench REST mw = requests.get("https://www.metabolomicsworkbench.org/rest/study/study_id/ST001234/analysis").json() - Network accessline 47
mw = requests.get("https://www.metabolomicsworkbench.org/rest/study/study_id/ST001234/analysis").json() - Network accessline 49
# HMDB metabolite data hmdb = requests.get("https://hmdb.ca/metabolites/HMDB0000001.xml").text # XML format - Network accessline 50
hmdb = requests.get("https://hmdb.ca/metabolites/HMDB0000001.xml").text # XML format - Network accessline 56
# MGnify: search analyses, download taxonomy/function profiles analyses = requests.get("https://www.ebi.ac.uk/metagenomics/api/v1/analyses", - Network accessline 57
analyses = requests.get("https://www.ebi.ac.uk/metagenomics/api/v1/analyses", - Network accessline 59
# Download OTU/taxonomy TSV for a in analyses["data"]: tax_url = f"https://www.ebi.ac.uk/metagenomics/api/v1/analyses/{a['id']}/downloads" - Network accessline 60
for a in analyses["data"]: tax_url = f"https://www.ebi.ac.uk/metagenomics/api/v1/analyses/{a['id']}/downloads" downloads = requests.get(tax_url).json() - Network accessline 61
tax_url = f"https://www.ebi.ac.uk/metagenomics/api/v1/analyses/{a['id']}/downloads" - Network accessline 62
downloads = requests.get(tax_url).json() - Network accessline 64
# GMREPO: gut microbiome phenotype associations gmrepo = requests.get("https://gmrepo.humangut.info/api/getAssociatedSpeciesByMeshID", - Network accessline 65
gmrepo = requests.get("https://gmrepo.humangut.info/api/getAssociatedSpeciesByMeshID", - Network accessline 74
offset = 0 while True: resp = requests.get("https://api.gbif.org/v1/occurrence/search", - Network accessline 75
while True: resp = requests.get("https://api.gbif.org/v1/occurrence/search", params={"scientificName": "Panthera tigris", "limit": 300, "offset": offset}).json() - Network accessline 76
resp = requests.get("https://api.gbif.org/v1/occurrence/search", - Network accessline 82
# iNaturalist observations obs = requests.get("https://api.inaturalist.org/v1/observations", - Network accessline 83
obs = requests.get("https://api.inaturalist.org/v1/observations", - Network accessline 90
# FlyBase gene data fb = requests.get("https://api.flybase.org/api/v1.0/gene/FBgn0000490").json() # dpp gene - Network accessline 91
fb = requests.get("https://api.flybase.org/api/v1.0/gene/FBgn0000490").json() # dpp gene - Network accessline 93
# WormBase gene info wb = requests.get("https://wormbase.org/rest/widget/gene/WBGene00006763/overview", - Network accessline 94
wb = requests.get("https://wormbase.org/rest/widget/gene/WBGene00006763/overview", - Network accessline 97
# ZFIN (zebrafish) gene expression zfin = requests.get("https://zfin.org/action/api/marker/ZDB-GENE-980526-166/expression").json() - Network accessline 98
zfin = requests.get("https://zfin.org/action/api/marker/ZDB-GENE-980526-166/expression").json() - Network accessline 100
# RGD (rat) — disease annotations for a gene rgd = requests.get("https://rest.rgd.mcw.edu/rgdws/genes/Tp53/9606").json() # human TP53 - Network accessline 101
rgd = requests.get("https://rest.rgd.mcw.edu/rgdws/genes/Tp53/9606").json() # human TP53 - Network accessline 107
# Reactome pathway participants pathway_id = "R-HSA-109582" # Hemostasis participants = requests.get(f"https://reactome.org/ContentService/data/participants/{pathway_id}").json() - Network accessline 109
participants = requests.get(f"https://reactome.org/ContentService/data/participants/{pathway_id}").json() - Network accessline 111
# STRING protein-protein interactions (bulk) proteins = "9606.ENSP00000269305%0d9606.ENSP00000344818" # TP53, MDM2 network = requests.get(f"https://string-db.org/api/json/network?identifiers={proteins}&species=9606").json() - Network accessline 113
network = requests.get(f"https://string-db.org/api/json/network?identifiers={proteins}&species=9606").json() - Network accessline 115
# BioGRID interactions for a gene (tab-delimited bulk) url = "https://webservice.thebiogrid.org/interactions/?searchNames=true&geneList=BRCA1&taxId=9606&format=json&accesskey=YOUR_KEY" - Network accessline 116
url = "https://webservice.thebiogrid.org/interactions/?searchNames=true&geneList=BRCA1&taxId=9606&format=json&accesskey=YOUR_KEY" - Network accessline 118
# WikiPathways: download pathway as GPML or GMT gpml = requests.get("https://www.wikipathways.org/wikipathways/wpi/wpi.php?action=downloadFile&type=gpml&pwTitle=Pathway:WP254").text - Network accessline 119
gpml = requests.get("https://www.wikipathways.org/wikipathways/wpi/wpi.php?action=downloadFile&type=gpml&pwTitle=Pathway:WP254").text - Network accessline 125
# OLS term search + hierarchy traversal terms = requests.get("https://www.ebi.ac.uk/ols4/api/search", - Network accessline 126
terms = requests.get("https://www.ebi.ac.uk/ols4/api/search", - Network accessline 129
# Get children of a GO term children = requests.get("https://www.ebi.ac.uk/ols4/api/ontologies/go/terms/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FGO_0006915/children").json() - Network accessline 130
children = requests.get("https://www.ebi.ac.uk/ols4/api/ontologies/go/terms/http%253A%252F%252Fpurl.obolibrary.org%252Fobo%252FGO_0006915/children").json() - Network accessline 132
# HPO: phenotype-to-disease annotations hpo = requests.get("https://hpo.jax.org/api/hpo/term/HP:0001250/diseases").json() - Network accessline 133
hpo = requests.get("https://hpo.jax.org/api/hpo/term/HP:0001250/diseases").json() - Network accessline 135
# Gene Ontology annotations bulk (GAF format) # Download from: http://current.geneontology.org/annotations/goa_human.gaf.gz - Network accessline 136
# Download from: http://current.geneontology.org/annotations/goa_human.gaf.gz - Network accessline 142
# IEDB epitope search epitopes = requests.get("https://query-api.iedb.org/epitope_search", - Network accessline 143
epitopes = requests.get("https://query-api.iedb.org/epitope_search", - Network accessline 146
# VDJdb: T-cell receptor specificity database vdjdb = pd.read_csv("https://raw.githubusercontent.com/antigenomics/vdjdb-db/master/latest-version.zip", - Network accessline 147
vdjdb = pd.read_csv("https://raw.githubusercontent.com/antigenomics/vdjdb-db/master/latest-version.zip", - Network accessline 150
# ImmPort shared data (requires registration) # Search at: https://www.immport.org/shared/search - Network accessline 151
# Search at: https://www.immport.org/shared/search - Network accessline 157
# DGIdb drug-gene interactions dgi = requests.get("https://dgidb.org/api/v2/interactions.json", - Network accessline 158
dgi = requests.get("https://dgidb.org/api/v2/interactions.json", - Network accessline 161
# PharmGKB clinical annotations for a gene pgkb = requests.get("https://api.pharmgkb.org/v1/data/clinicalAnnotation", - Network accessline 162
pgkb = requests.get("https://api.pharmgkb.org/v1/data/clinicalAnnotation", - Network accessline 165
# SIDER side effect frequencies (bulk download) # Download from: http://sideeffects.embl.de/media/download/meddra_freq.tsv.gz - Network accessline 166
# Download from: http://sideeffects.embl.de/media/download/meddra_freq.tsv.gz - Network accessline 172
# TCIA: cancer imaging collections collections = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getCollectionValues").json() - Network accessline 173
collections = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getCollectionValues").json() - Network accessline 174
# Get series for a patient series = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getSeries", params={"Collection": "TCGA-BRCA", "PatientID": "TCGA-A1-A0SB"}).json() - Network accessline 175
series = requests.get("https://services.cancerimagingarchive.net/nbia-api/services/v1/getSeries", - Network accessline 178
# Human Protein Atlas: tissue expression hpa = requests.get("https://www.proteinatlas.org/api/search_download.php?search=TP53&format=json&columns=g,t,up").json() - Network accessline 179
hpa = requests.get("https://www.proteinatlas.org/api/search_download.php?search=TP53&format=json&columns=g,t,up").json() - Network accessline 181
# Allen Brain Atlas: gene expression in brain regions aba = requests.get("https://api.brain-map.org/api/v2/data/query.json?criteria=model::Gene,rma::criteria,[acronym$eq'BDNF']").json() - Network accessline 182
aba = requests.get("https://api.brain-map.org/api/v2/data/query.json?criteria=model::Gene,rma::criteria,[acronym$eq'BDNF']").json() - Network accessline 188
# Download PDB/CIF structure files directly pdb_id = "1A2B" pdb_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.pdb").text - Network accessline 189
pdb_id = "1A2B" pdb_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.pdb").text cif_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif").text - Network accessline 190
pdb_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.pdb").text - Network accessline 191
cif_content = requests.get(f"https://files.rcsb.org/download/{pdb_id}.cif").text - Network accessline 193
# AlphaFold predicted structure by UniProt ID uniprot_id = "P04637" # TP53 af_pdb = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb").text - Network accessline 194
uniprot_id = "P04637" # TP53 af_pdb = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb").text af_cif = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.cif").text - Network accessline 195
af_pdb = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.pdb").text - Network accessline 196
af_cif = requests.get(f"https://alphafold.ebi.ac.uk/files/AF-{uniprot_id}-F1-model_v4.cif").text - Network accessline 198
# RCSB advanced search (GraphQL) for bulk queries query = {"query": {"type": "terminal", "service": "text", "parameters": {"attribute": "rcsb_entity_source_organism.taxonomy_lineage.name", "operator": "exact_match", "value": "Homo sapiens"} - Network accessline 200
results = requests.post("https://search.rcsb.org/rcsbsearch/v2/query", json=query).json() - Network accessline 206
# ClinVar bulk download (variant_summary, ~200MB) # df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") - Network accessline 207
# df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") - Network accessline 209
# CIViC GraphQL API — all evidence items for a gene query = '{"query": "{ gene(entrezId: 673) { name variants { nodes { name evidenceItems { nodes { description evidenceLevel } } } } } }"}' civic = requests.post("https://civicdb.org/api/gra - Network accessline 211
civic = requests.post("https://civicdb.org/api/graphql", json={"query": query}).json() - Network accessline 213
# ClinGen allele registry allele = requests.get("https://reg.clinicalgenome.org/allele?hgvs=NM_000059.4:c.68_69del").json() - Network accessline 214
allele = requests.get("https://reg.clinicalgenome.org/allele?hgvs=NM_000059.4:c.68_69del").json() - Network accessline 226
# ARCHS4 gene expression (pre-computed from SRA) archs4 = requests.get("https://maayanlab.cloud/archs4/search/loadExpressionTSV.php", - Network accessline 227
archs4 = requests.get("https://maayanlab.cloud/archs4/search/loadExpressionTSV.php", - Network accessline 230
# Without cellxgene_census: download h5ad directly from cellxgene data portal # Browse collections at https://cellxgene.cziscience.com/collections - Network accessline 231
# Browse collections at https://cellxgene.cziscience.com/collections - Network accessline 237
# CTD: chemical-gene-disease interactions ctd = requests.get("https://ctdbase.org/tools/batchQuery.go", - Network accessline 238
ctd = requests.get("https://ctdbase.org/tools/batchQuery.go", - Network accessline 241
# EPA CompTox Dashboard comptox = requests.get("https://comptox.epa.gov/dashboard/api/search/chemical/equal/Bisphenol%20A").json() - Network accessline 242
comptox = requests.get("https://comptox.epa.gov/dashboard/api/search/chemical/equal/Bisphenol%20A").json()
skills/tooluniverse-database-logins/SKILL.md
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| Login | Unlocks | Where to get it | Cost / wait | |---|---|---|---| | `NCBI_API_KEY` (+ `NCBI_EMAIL`) | Faster PubMed, Gene, Protein and other NCBI tools (they work without it, slower) | https://account.ncbi.nlm.nih.gov/settings/ → API Ke - Network accessline 20
|---|---|---|---| | `NCBI_API_KEY` (+ `NCBI_EMAIL`) | Faster PubMed, Gene, Protein and other NCBI tools (they work without it, slower) | https://account.ncbi.nlm.nih.gov/settings/ → API Key Management | Free, ~2 minutes | | `OMIM_API_KEY` | - Network accessline 21
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| `HF_TOKEN` | Hugging Face–hosted models and datasets | https://huggingface.co/settings/tokens | Free account | - Network accessline 29
The full list, with steps for each, is on https://connect.aiscientist.tools/credentials (search box). Before the user signs up for anything paid, metered, or requiring a license or an institutional claim, say so and let them decide. - Network accessline 39
1. Open https://connect.aiscientist.tools/credentials. - Access to secretsline 44
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skills/tooluniverse-dataset-discovery/SKILL.md
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# --- Download from URL first, then parse --- resp = requests.get(url, timeout=120) - Network accessline 111
resp = requests.get(url, timeout=120) - Network accessline 129
offset = 0 while True: resp = requests.get(f"{api_url}?offset={offset}&limit=100", timeout=30) - Network accessline 130
while True: resp = requests.get(f"{api_url}?offset={offset}&limit=100", timeout=30) batch = resp.json().get("data", []) - Network accessline 131
resp = requests.get(f"{api_url}?offset={offset}&limit=100", timeout=30)
skills/tooluniverse-diagnostic-test-evaluation/scripts/roc_analysis.py
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#!/usr/bin/env python3
skills/tooluniverse-diagnostic-test-evaluation/test_roc_analysis.py
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skills/tooluniverse-disease-research/SKILL.md
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"max_results": 3, }) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 78
"max_results": 3, }) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 79
}) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], # "curies": ["MONDO:0004979"], "confidence": "HIGH", "source": "zooma", ...}] - Network accessline 79
}) # -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], # "curies": ["MONDO:0004979"], "confidence": "HIGH", "source": "zooma", ...}] - Network accessline 80
# -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 80
# -> [{"semantic_tags": ["http://purl.obolibrary.org/obo/MONDO_0004979"], - Network accessline 86
# Inspect which curated datasources back ZOOMA annotations (for provenance): tu.run_tool("ZOOMA_list_datasources", {}) # -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...] - Network accessline 86
# Inspect which curated datasources back ZOOMA annotations (for provenance): tu.run_tool("ZOOMA_list_datasources", {}) # -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...] - Network accessline 88
# -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...] - Network accessline 88
# -> [{"name": "eva-clinvar", "type": "DATABASE", "uri": "https://www.ebi.ac.uk/eva"}, ...]
skills/tooluniverse-disease-research/TOOLS_REFERENCE.md
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skills/tooluniverse-dose-response/scripts/fit_dose_response.py
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#!/usr/bin/env python3
skills/tooluniverse-dose-response/test_fit_dose_response.py
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skills/tooluniverse-drug-drug-interaction/.env.template
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skills/tooluniverse-drug-drug-interaction/ddi_pipeline.py
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#!/usr/bin/env python3
skills/tooluniverse-drug-drug-interaction/ddi_working_example.py
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#!/usr/bin/env python3
skills/tooluniverse-drug-drug-interaction/python_implementation.py
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#!/usr/bin/env python3
skills/tooluniverse-drug-drug-interaction/scripts/pharmacology_ref.py
skills/tooluniverse-drug-repurposing/REFERENCE.md
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- **ToolUniverse Documentation**: https://zitniklab.hms.harvard.edu/ToolUniverse/ - Network accessline 670
- **Tool Catalog**: https://zitniklab.hms.harvard.edu/ToolUniverse/tools/ - Network accessline 671
- **DrugBank**: https://go.drugbank.com/ - Network accessline 672
- **OpenTargets**: https://platform.opentargets.org/ - Network accessline 673
- **ChEMBL**: https://www.ebi.ac.uk/chembl/ - Network accessline 674
- **OpenFDA**: https://open.fda.gov/ - Network accessline 675
- **PubMed**: https://pubmed.ncbi.nlm.nih.gov/
skills/tooluniverse-drug-repurposing/SKILL.md
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| Disease not found | Try synonyms or EFO ID lookup | | No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key st - Network accessline 280
| Disease not found | Try synonyms or EFO ID lookup | | No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key st - Network accessline 281
| No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key still activating — verify at https://data.uspto.gov/myod - Network accessline 281
| No drugs for target | Check HUGO nomenclature, expand to pathway-level, try similar targets | | Patent tools return HTTP 403 | `USPTO_API_KEY` missing/invalid, or a newly issued key still activating — verify at https://data.uspto.gov/myod - Network accessline 282
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skills/tooluniverse-drug-research/EXAMPLES.md
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| Identifier | Value | Link | |------------|-------|------| | **PubChem CID** | 4091 | [Link](https://pubchem.ncbi.nlm.nih.gov/compound/4091) | - Network accessline 39
|------------|-------|------| | **PubChem CID** | 4091 | [Link](https://pubchem.ncbi.nlm.nih.gov/compound/4091) | | **ChEMBL ID** | CHEMBL1431 | [Link](https://www.ebi.ac.uk/chembl/compound_report_card/CHEMBL1431/) | - Network accessline 40
| **PubChem CID** | 4091 | [Link](https://pubchem.ncbi.nlm.nih.gov/compound/4091) | - Network accessline 41
| **ChEMBL ID** | CHEMBL1431 | [Link](https://www.ebi.ac.uk/chembl/compound_report_card/CHEMBL1431/) | - Network accessline 42
| **RxNorm CUI** | 6809 | RxNorm | | **PharmGKB ID** | PA450657 | [Link](https://www.pharmgkb.org/chemical/PA450657) | | **DailyMed** | Multiple | FDA approved | - Network accessline 43
| **PharmGKB ID** | PA450657 | [Link](https://www.pharmgkb.org/chemical/PA450657) |
skills/tooluniverse-drug-synergy/scripts/synergy_reference.py
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#!/usr/bin/env python3
skills/tooluniverse-drug-synergy/test_synergy_reference.py
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skills/tooluniverse-drug-target-validation/test_skill.py
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#!/usr/bin/env python3
skills/tooluniverse-ecology-biodiversity/SKILL.md
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skills/tooluniverse-electron-microscopy/SKILL.md
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IDR (Image Data Resource, https://idr.openmicroscopy.org) is the reference repository for *light* microscopy and high-content screening data — distinct from the EM databases above. Use it to find published imaging studies and images by cura
skills/tooluniverse-enzyme-kinetics/scripts/fit_michaelis_menten.py
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#!/usr/bin/env python3
skills/tooluniverse-enzyme-kinetics/test_fit_michaelis_menten.py
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skills/tooluniverse-epidemiological-analysis/SKILL.md
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# Generic download helper — adapt URL and format to your source def download_and_parse(url, fmt="csv"): r = requests.get(url, timeout=120) - Network accessline 69
def download_and_parse(url, fmt="csv"): r = requests.get(url, timeout=120) content = io.BytesIO(r.content) - Network accessline 70
r = requests.get(url, timeout=120) - Access to secretsline 111
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offset = 0 while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30) - Network accessline 146
while True: resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30) batch = resp.json().get("data", []) - Network accessline 147
resp = requests.get(f"{api_url}?offset={offset}&limit=500", timeout=30)
skills/tooluniverse-epigenomics/.env.template
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skills/tooluniverse-epigenomics/scripts/methylation_density.py
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#!/usr/bin/env python3
skills/tooluniverse-epigenomics/test_skill.py
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#!/usr/bin/env python3
skills/tooluniverse-fastq-qc/scripts/run_fastq_qc.py
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#!/usr/bin/env python3
skills/tooluniverse-fastq-qc/test_skill.py
skills/tooluniverse-gene-disease-association/SKILL.md
skills/tooluniverse-gene-enrichment/.env.template
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skills/tooluniverse-gene-enrichment/SKILL.md
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gseapy documentation: https://gseapy.readthedocs.io/ - Network accessline 498
PANTHER API: http://pantherdb.org/services/oai/pantherdb/ - Network accessline 499
STRING API: https://string-db.org/cgi/help?sessionId=&subpage=api - Network accessline 500
Reactome Analysis: https://reactome.org/AnalysisService/
skills/tooluniverse-gene-enrichment/references/troubleshooting.md
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1. **Check gseapy documentation**: https://gseapy.readthedocs.io/
skills/tooluniverse-gene-enrichment/scripts/condition_enrichment_screen.py
skills/tooluniverse-gene-enrichment/scripts/enrichgo_runner.py
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#!/usr/bin/env python3
skills/tooluniverse-gene-enrichment/scripts/format_enrichment_output.py
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#!/usr/bin/env python3
skills/tooluniverse-gene-enrichment/scripts/gseapy_enrichment_runner.py
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#!/usr/bin/env python3
skills/tooluniverse-gene-enrichment/test_gene_enrichment.py
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#!/usr/bin/env python3
skills/tooluniverse-gene-liability/test_skill.py
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skills/tooluniverse-glycobiology/references/glygen_tool_reference.md
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"species": ["Homo sapiens", "..."], "composition": [ {"name": "N-Acetylhexosamine", "residue": "hexnac", "count": 4, "cid": "...", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/..."}, - Network accessline 26
"composition": [ {"name": "N-Acetylhexosamine", "residue": "hexnac", "count": 4, "cid": "...", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/..."}, {"name": "N-Acetyl-Neuraminic Acid", "residue": "neuac", "count": 2, "cid": - Network accessline 27
{"name": "N-Acetylhexosamine", "residue": "hexnac", "count": 4, "cid": "...", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/..."}, - Network accessline 28
{"name": "N-Acetyl-Neuraminic Acid", "residue": "neuac", "count": 2, "cid": "439197", "url": "https://pubchem.ncbi.nlm.nih.gov/compound/439197"} - Network accessline 159
"glycosylation": ["..."], "snv": ["..."], "species": [{"common_name": "Human", "glygen_name": "Human", "reference_species": "Homo sapiens [9606]", "url": "https://www.uniprot.org/uniprotkb/P14210"}], - Network accessline 160
"snv": ["..."], "species": [{"common_name": "Human", "glygen_name": "Human", "reference_species": "Homo sapiens [9606]", "url": "https://www.uniprot.org/uniprotkb/P14210"}], "all_sites_count": 3, - Network accessline 161
"species": [{"common_name": "Human", "glygen_name": "Human", "reference_species": "Homo sapiens [9606]", "url": "https://www.uniprot.org/uniprotkb/P14210"}],
skills/tooluniverse-gpcr-structural-pharmacology/SKILL.md
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- GPCRdb: https://gpcrdb.org — standardized GPCR data with generic numbering - Network accessline 294
- SAbDab: https://opig.stats.ox.ac.uk/webapps/newsabdab/sabdab — structural antibody database - Network accessline 295
- PDBePISA: https://www.ebi.ac.uk/pdbe/pisa — protein interface analysis
skills/tooluniverse-gwas-drug-discovery/QUICK_START.md
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# Or from source git clone https://github.com/example/tooluniverse.git - Network accessline 31
git clone https://github.com/example/tooluniverse.git - Network accessline 121
// Initialize MCP client const client = new MCPClient({ serverUrl: "http://localhost:3000/tooluniverse" - Network accessline 122
const client = new MCPClient({ serverUrl: "http://localhost:3000/tooluniverse" }); - Network accessline 123
serverUrl: "http://localhost:3000/tooluniverse" - Network accessline 1021
- [GWAS Catalog Documentation](https://www.ebi.ac.uk/gwas/docs) - Network accessline 1022
- [Open Targets Platform](https://platform.opentargets.org/) - Network accessline 1023
- [ChEMBL Database](https://www.ebi.ac.uk/chembl/) - Network accessline 1024
- [ToolUniverse Documentation](https://docs.tooluniverse.org)
skills/tooluniverse-gwas-drug-discovery/python_implementation.py
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skills/tooluniverse-gwas-drug-discovery/test_gwas_drug_tools.py
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skills/tooluniverse-gwas-drug-discovery/test_gwas_drug_tools_v2.py
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skills/tooluniverse-gwas-finemapping/.env.template
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skills/tooluniverse-gwas-finemapping/SKILL.md
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**Where to get the raw inputs**: these three tools compute on arrays you supply — they do not fetch summary statistics themselves. `src/tooluniverse/data/gwas_sumstats_tools.json`'s 3 tools (`GWASSumStats_list_studies`, `GWASSumStats_get_tr
skills/tooluniverse-gwas-finemapping/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-gwas-finemapping/test_skill_comprehensive.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-gwas-snp-interpretation/.env.template
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[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
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[Credential-related source omitted]
skills/tooluniverse-gwas-snp-interpretation/SKILL.md
skills/tooluniverse-gwas-snp-interpretation/python_implementation.py
- Executable scriptline 1
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skills/tooluniverse-gwas-snp-interpretation/test_gwas_snp_tools.py
- Executable scriptline 1
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skills/tooluniverse-gwas-snp-interpretation/test_gwas_snp_tools_simple.py
- Executable scriptline 1
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skills/tooluniverse-gwas-snp-interpretation/test_skill_comprehensive.py
- Executable scriptline 1
Executable file
skills/tooluniverse-gwas-study-explorer/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
skills/tooluniverse-gwas-study-explorer/python_implementation.py
- Executable scriptline 1
Executable file
skills/tooluniverse-gwas-study-explorer/test_skill_comprehensive.py
- Executable scriptline 1
Executable file
skills/tooluniverse-gwas-trait-to-gene/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
skills/tooluniverse-gwas-trait-to-gene/SKILL.md
- Network accessline 224
# Download full GWAS Catalog (all associations, ~37MB TSV) url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 225
url = "https://www.ebi.ac.uk/gwas/api/search/downloads/alternative" - Network accessline 231
# Per-study associations via REST study_id = "GCST001234" assocs = requests.get(f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations").json() - Network accessline 233
assocs = requests.get(f"https://www.ebi.ac.uk/gwas/rest/api/studies/{study_id}/associations").json() - Network accessline 274
**GWAS Catalog** - Curator: EBI and NHGRI - URL: https://www.ebi.ac.uk/gwas/ - Network accessline 275
- Curator: EBI and NHGRI - URL: https://www.ebi.ac.uk/gwas/ - Coverage: 100,000+ publications, 500,000+ associations - Network accessline 276
- URL: https://www.ebi.ac.uk/gwas/ - Network accessline 280
**Open Targets Genetics** - Curator: Open Targets consortium - URL: https://genetics.opentargets.org/ - Network accessline 281
- Curator: Open Targets consortium - URL: https://genetics.opentargets.org/ - Coverage: Fine-mapped GWAS, L2G predictions, QTL colocalization - Network accessline 282
- URL: https://genetics.opentargets.org/
skills/tooluniverse-gwas-trait-to-gene/python_implementation.py
- Executable scriptline 1
Executable file
skills/tooluniverse-gwas-trait-to-gene/test_gwas_tools.py
- Executable scriptline 1
Executable file
skills/tooluniverse-gwas-trait-to-gene/test_skill_comprehensive.py
- Executable scriptline 1
Executable file
skills/tooluniverse-image-analysis/scripts/batch_process.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-image-analysis/scripts/measure_fluorescence.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-image-analysis/scripts/segment_cells.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-image-analysis/test_image_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 30
DATA_DIR = "/tmp/bixbench_image_data" BASE_URL = "https://huggingface.co/datasets/futurehouse/BixBench/resolve/main/" - Network accessline 31
BASE_URL = "https://huggingface.co/datasets/futurehouse/BixBench/resolve/main/" - Network accessline 48
zip_name = f"CapsuleFolder-{uuid}.zip" url = BASE_URL + zip_name r = requests.get(url) - Network accessline 49
url = BASE_URL + zip_name r = requests.get(url) r.raise_for_status() - Network accessline 50
r = requests.get(url)
skills/tooluniverse-immune-repertoire-analysis/ANALYSIS_DETAILS.md
skills/tooluniverse-immunotherapy-response-prediction/SKILL.md
- Network accessline 201
- OpenTargets: https://platform.opentargets.org - Network accessline 202
- CIViC: https://civicdb.org - Network accessline 203
- FDA Drug Labels: https://dailymed.nlm.nih.gov - Network accessline 204
- DrugBank: https://go.drugbank.com - Network accessline 205
- PubMed: https://pubmed.ncbi.nlm.nih.gov - Network accessline 206
- IEDB: https://www.iedb.org - Network accessline 207
- HPA: https://www.proteinatlas.org - Network accessline 208
- cBioPortal: https://www.cbioportal.org
skills/tooluniverse-immunotherapy-response-prediction/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-infectious-disease/CHECKLIST.md
- Access to secretsline 33
[Credential-related source omitted]
skills/tooluniverse-infectious-disease/SKILL.md
skills/tooluniverse-infectious-disease/TOOLS_REFERENCE.md
skills/tooluniverse-inorganic-physical-chemistry/scripts/equilibrium_solver.py
skills/tooluniverse-install-skills/SKILL.md
- Network accessline 33
# 1. Download skills from GitHub (shallow, sparse — only skills/ folder) git clone --depth 1 --filter=blob:none --sparse \ https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Network accessline 34
git clone --depth 1 --filter=blob:none --sparse https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Network accessline 35
https://github.com/mims-harvard/ToolUniverse.git /tmp/tu-skills - Dangerous shell operationline 49
rm -rf /tmp/tu-skills
skills/tooluniverse-lab-data-standardization/references/schema_overview.md
- Network accessline 10
"tier": "native" | "fallback", "note": "<present only for tier=fallback: 'not ASM-validated, best-effort structural parse'>", "manifest": "http://purl.allotrope.org/... (Tier 1 only)", - Network accessline 11
"note": "<present only for tier=fallback: 'not ASM-validated, best-effort structural parse'>", "manifest": "http://purl.allotrope.org/... (Tier 1 only)", "source_file": "plate_reader_run1.csv", - Network accessline 12
"manifest": "http://purl.allotrope.org/... (Tier 1 only)",
skills/tooluniverse-lab-data-standardization/scripts/convert_instrument_data.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-lab-data-standardization/scripts/flatten_to_csv.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-lab-data-standardization/scripts/validate_output.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-lab-protocols/SKILL.md
- Access to secretsline 3
[Credential-related source omitted] - Network accessline 13
[Credential-related source omitted] - Access to secretsline 13
[Credential-related source omitted] - Network accessline 57
- Search (`ProtocolsIO_search_protocols`) uses the v3 API; per-protocol detail and steps use the newer v4 API — this is a real API-versioning quirk in protocols.io itself, not an inconsistency in this tool. - **Live-verified**: an invalid/m - Network accessline 59
- The response-body schemas here (protocol fields, step structure) were built directly from protocols.io's published API documentation (https://apidoc.protocols.io/), not live-verified end-to-end in this environment (no real API key was ava
skills/tooluniverse-literature-deep-research/EXAMPLES.md
- Network accessline 439
"citation_count": 612, "oa_status": "gold", "oa_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704954/" - Network accessline 440
"oa_status": "gold", "oa_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704954/" } - Network accessline 441
"oa_url": "https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5704954/"
skills/tooluniverse-literature-deep-research/FULLTEXT_STRATEGY.md
- Network accessline 84
get_webpage_text_from_url(url="https://doi.org/10.1016/...")
skills/tooluniverse-literature-deep-research/REPORT_TEMPLATE.md
skills/tooluniverse-literature-deep-research/SKILL.md
- Network accessline 167
**Citation cross-references — naming collision, read carefully**: `src/tooluniverse/data/europepmc_citations_tools.json` defines `EPMC_get_citations`, `EPMC_get_references`, and `EuropePMC_get_article_datalinks` — do NOT confuse these with - Access to secretsline 171
[Credential-related source omitted]
skills/tooluniverse-literature-deep-research/TOOL_NAMES_REFERENCE.md
- Access to secretsline 63
[Credential-related source omitted]
skills/tooluniverse-medical-imaging-radiology/SKILL.md
- Network accessline 23
what imaging exists — not the images themselves. If the user wants the actual pixels, tell them the DICOM download UI/API is a separate TCIA service (`https://www.cancerimagingarchive.net/`) outside this tool set. - Network accessline 25
service (`https://www.cancerimagingarchive.net/`) outside this tool set.
skills/tooluniverse-medical-imaging-radiology/references/tcia_tool_reference.md
- Network accessline 3
All 10 tools live-tested against the real TCIA API (`https://services.cancerimagingarchive.net/nbia-api/`) on 2026-09-17. - Network accessline 4
(`https://services.cancerimagingarchive.net/nbia-api/`) on 2026-09-17. - Network accessline 97
"ImageCount": 2, "FileSize": 16357620, "LicenseName": "Creative Commons Attribution 3.0 Unported License", "LicenseURI": "http://creativecommons.org/licenses/by/3.0/", - Network accessline 98
"LicenseName": "Creative Commons Attribution 3.0 Unported License", "LicenseURI": "http://creativecommons.org/licenses/by/3.0/", "CollectionURI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX"} - Network accessline 99
"LicenseURI": "http://creativecommons.org/licenses/by/3.0/", - Network accessline 100
"CollectionURI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX"} - Network accessline 172
{"Series UID": "1.3.6.1.4.1.14519.5.2.1.6279.6001.141365756818074696859567662357", "Collection": "LIDC-IDRI", "Data Description URI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX", - Network accessline 173
"Collection": "LIDC-IDRI", "Data Description URI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX", "Subject ID": "LIDC-IDRI-0001", - Network accessline 174
"Data Description URI": "https://doi.org/10.7937/K9/TCIA.2015.LO9QL9SX",
skills/tooluniverse-meta-analysis/scripts/meta_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-meta-analysis/test_meta_analysis.py
- Executable scriptline 1
Executable file
skills/tooluniverse-metabolomics-analysis/SKILL.md
- Network accessline 206
**Methods**: - MetaboAnalyst: https://doi.org/10.1093/nar/gkab382 - Network accessline 207
- MetaboAnalyst: https://doi.org/10.1093/nar/gkab382 - Network accessline 208
- XCMS: https://doi.org/10.1021/ac051437y - Network accessline 209
- MSEA: https://doi.org/10.1186/1471-2105-11-395 - Network accessline 211
**Databases**: - HMDB: https://hmdb.ca - Network accessline 212
- HMDB: https://hmdb.ca - Network accessline 213
- KEGG Compound: https://www.genome.jp/kegg/compound/ - Network accessline 214
- Reactome: https://reactome.org
skills/tooluniverse-metabolomics/.env.template
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[Credential-related source omitted] - Access to secretsline 4
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[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
skills/tooluniverse-metabolomics/QUICK_START.md
- Access to secretsline 415
[Credential-related source omitted] - Access to secretsline 423
[Credential-related source omitted] - Access to secretsline 424
[Credential-related source omitted] - Network accessline 503
- **ToolUniverse Documentation**: https://github.com/mims-harvard/ToolUniverse - Network accessline 504
- **HMDB Database**: https://hmdb.ca - Network accessline 505
- **MetaboLights**: https://www.ebi.ac.uk/metabolights/ - Network accessline 506
- **Metabolomics Workbench**: https://www.metabolomicsworkbench.org - Network accessline 507
- **PubChem**: https://pubchem.ncbi.nlm.nih.gov
skills/tooluniverse-metabolomics/diabetes_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-metabolomics/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-metabolomics/scripts/metabolism_ref.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-metabolomics/test1_metabolites.md
- Network accessline 13
**Formula**: C6H12O6 **Molecular Weight**: 180.16 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 15
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 20
**Formula**: C3H5O3- **Molecular Weight**: 89.07 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=lactate&searcher=metabolites - Network accessline 22
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=lactate&searcher=metabolites
skills/tooluniverse-metabolomics/test2_study.md
skills/tooluniverse-metabolomics/test4_comprehensive.md
- Network accessline 15
**Formula**: C6H12O6 **Molecular Weight**: 180.16 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 17
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=glucose&searcher=metabolites - Network accessline 22
**Formula**: C3H3O3- **Molecular Weight**: 87.05 **HMDB Search URL**: https://hmdb.ca/unearth/q?query=pyruvate&searcher=metabolites - Network accessline 24
**HMDB Search URL**: https://hmdb.ca/unearth/q?query=pyruvate&searcher=metabolites - Network accessline 30
**Release Date**: N/A **Modified Time**: 2025-09-02T13:34:14.536288 **HTTP URL**: http://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1 - Network accessline 32
**HTTP URL**: http://ftp.ebi.ac.uk/pub/databases/metabolights/studies/public/MTBLS1
skills/tooluniverse-metabolomics/test_detailed.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-metabolomics/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-metabolomics/test_tools_debug.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-ml-inference-utility/SKILL.md
- Network accessline 35
1. **Check for `HF_TOKEN` before promising a result.** If it's unset, tell the user plainly that HuggingFace's serverless inference now requires a token, point them to https://huggingface.co/settings/tokens to create a free one - Network accessline 36
user plainly that HuggingFace's serverless inference now requires a token, point them to https://huggingface.co/settings/tokens to create a free one (a `read`-scope token is sufficient), and stop rather than guessing an output. - Network accessline 37
point them to https://huggingface.co/settings/tokens to create a free one - Network accessline 220
Both **require `REPLICATE_API_TOKEN`** (register at https://replicate.com/account/api-tokens) — **live-verified**: with no token - Network accessline 221
https://replicate.com/account/api-tokens) — **live-verified**: with no token
skills/tooluniverse-ml-inference-utility/references/hf_inference_tool_reference.md
skills/tooluniverse-molecular-cloning/scripts/cloning_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-molecular-cloning/test_cloning_qc.py
- Executable scriptline 1
Executable file
skills/tooluniverse-multi-omics-integration/SKILL.md
skills/tooluniverse-multiomic-disease-characterization/.env.template
- Access to secretsline 2
[Credential-related source omitted] - Access to secretsline 4
[Credential-related source omitted] - Access to secretsline 12
[Credential-related source omitted] - Access to secretsline 16
[Credential-related source omitted] - Access to secretsline 18
[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
skills/tooluniverse-multiomic-disease-characterization/test_multiomic_disease.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-network-pharmacology/scripts/network_proximity.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 36
STRING_LINKS = "https://stringdb-downloads.org/download/protein.links.v12.0/9606.protein.links.v12.0.txt.gz" - Network accessline 37
STRING_INFO = "https://stringdb-downloads.org/download/protein.info.v12.0/9606.protein.info.v12.0.txt.gz" - Network accessline 45
if not os.path.exists(path) or os.path.getsize(path) == 0: sys.stderr.write(f"Downloading {name} (one-time)...\n") urllib.request.urlretrieve(url, path) - Network accessline 46
sys.stderr.write(f"Downloading {name} (one-time)...\n") urllib.request.urlretrieve(url, path) return path - Network accessline 47
urllib.request.urlretrieve(url, path)
skills/tooluniverse-network-pharmacology/test_network_pharmacology.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-neurosynth-meta-analysis/SKILL.md
- Network accessline 74
- `references/method.md` — the statistical method in more detail, data provenance, and known limitations - Neurosynth: https://neurosynth.org - Network accessline 75
- Neurosynth: https://neurosynth.org - Network accessline 76
- Data source: https://github.com/neurosynth/neurosynth-data - Network accessline 77
- For a full ALE/MKDA meta-analysis pipeline instead of this simplified proxy: NiMARE (https://nimare.readthedocs.io)
skills/tooluniverse-neurosynth-meta-analysis/references/method.md
- Network accessline 5
All data comes from the public `neurosynth/neurosynth-data` GitHub repository (https://github.com/neurosynth/neurosynth-data), maintained by the Neurosynth - Network accessline 6
(https://github.com/neurosynth/neurosynth-data), maintained by the Neurosynth - Network accessline 89
if a fetch 404s. - **For anything publication-grade**: use NiMARE (https://nimare.readthedocs.io), which implements real ALE/MKDA with - Network accessline 90
- **For anything publication-grade**: use NiMARE (https://nimare.readthedocs.io), which implements real ALE/MKDA with proper cluster-level family-wise-error correction against this same - Network accessline 91
(https://nimare.readthedocs.io), which implements real ALE/MKDA with
skills/tooluniverse-neurosynth-meta-analysis/scripts/_neurosynth_data.py
- Executable scriptline 1
Executable file
skills/tooluniverse-neurosynth-meta-analysis/scripts/decode_coordinate.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-neurosynth-meta-analysis/scripts/download_data.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 30
RAW_BASE = "https://raw.githubusercontent.com/neurosynth/neurosynth-data/master" - Access to secretsline 41
[Credential-related source omitted] - Network accessline 51
print(f" downloading {url}") try: with urllib.request.urlopen(url, timeout=60) as resp: - Network accessline 52
try: with urllib.request.urlopen(url, timeout=60) as resp: if resp.status != 200: - Network accessline 53
with urllib.request.urlopen(url, timeout=60) as resp: - Network accessline 92
"\nOne or more downloads failed. Do NOT proceed with analysis on " "partial data - re-run this script, or check " "https://github.com/neurosynth/neurosynth-data for the current " - Network accessline 93
"partial data - re-run this script, or check " "https://github.com/neurosynth/neurosynth-data for the current " "file names if the version number has moved on.", - Network accessline 94
"https://github.com/neurosynth/neurosynth-data for the current "
skills/tooluniverse-neurosynth-meta-analysis/scripts/search_by_term.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-nfcore-pipelines/SKILL.md
- Network accessline 29
3. **Never invent a pipeline version.** nf-core pipelines are versioned and pinned with `-r <version>`. If you are not certain of the current stable release, tell the user to check `https://nf-co.re/<pipeline>/releases` - Network accessline 30
pinned with `-r <version>`. If you are not certain of the current stable release, tell the user to check `https://nf-co.re/<pipeline>/releases` rather than guessing a version number — the version numbers below were - Network accessline 31
release, tell the user to check `https://nf-co.re/<pipeline>/releases` - Network accessline 290
Apache-2.0. This skill orchestrates them but does not vendor or modify pipeline code. Always pin `-r <version>` and verify current releases at https://nf-co.re before a real run — the versions referenced in this skill's - Network accessline 291
pipeline code. Always pin `-r <version>` and verify current releases at https://nf-co.re before a real run — the versions referenced in this skill's reference files may lag behind the latest nf-core release. - Network accessline 292
https://nf-co.re before a real run — the versions referenced in this skill's
skills/tooluniverse-nfcore-pipelines/references/geo_sra_acquisition.md
- Network accessline 74
curl -L -o sample1.fastq.gz "<direct-url>"
skills/tooluniverse-nfcore-pipelines/references/pipelines/atacseq.md
skills/tooluniverse-nfcore-pipelines/references/pipelines/rnaseq.md
skills/tooluniverse-nfcore-pipelines/references/pipelines/sarek.md
skills/tooluniverse-nfcore-pipelines/references/troubleshooting.md
skills/tooluniverse-nfcore-pipelines/scripts/check_environment.py
- Executable scriptline 1
#!/usr/bin/env python3 - Dangerous shell operationline 48
" sudo systemctl start docker\n" - Dangerous shell operationline 50
" sudo usermod -aG docker $USER # then log out and back in\n" - Network accessline 58
return ( "Install Docker Engine (Linux):\n" " curl -fsSL https://get.docker.com | sh\n" - Network accessline 59
"Install Docker Engine (Linux):\n" " curl -fsSL https://get.docker.com | sh\n" " sudo usermod -aG docker $USER # then log out and back in\n" - Dangerous shell operationline 60
" curl -fsSL https://get.docker.com | sh\n" - Network accessline 60
" curl -fsSL https://get.docker.com | sh\n" - Dangerous shell operationline 61
" sudo usermod -aG docker $USER # then log out and back in\n" - Network accessline 61
" sudo usermod -aG docker $USER # then log out and back in\n" "See https://docs.docker.com/engine/install/ for distro-specific steps.\n" "If you cannot get root/sudo (e.g. a locked-down sandbox), Docker cannot " - Network accessline 62
"See https://docs.docker.com/engine/install/ for distro-specific steps.\n" - Dangerous shell operationline 63
"If you cannot get root/sudo (e.g. a locked-down sandbox), Docker cannot " - Network accessline 64
"be installed here — this skill cannot run a real pipeline in that case." ) return "Install Docker Desktop: https://docs.docker.com/get-docker/" - Network accessline 66
return "Install Docker Desktop: https://docs.docker.com/get-docker/" - Network accessline 74
"Nextflow is not installed.", "Install Nextflow:\n" " curl -s https://get.nextflow.io | bash\n" - Network accessline 75
"Install Nextflow:\n" " curl -s https://get.nextflow.io | bash\n" " mkdir -p ~/bin && mv nextflow ~/bin/\n" - Dangerous shell operationline 76
" curl -s https://get.nextflow.io | bash\n" - Network accessline 76
" curl -s https://get.nextflow.io | bash\n" - Dangerous shell operationline 106
" sudo apt-get update && sudo apt-get install -y openjdk-17-jre-headless\n" - Network accessline 106
" sudo apt-get update && sudo apt-get install -y openjdk-17-jre-headless\n" "Other distros: use your package manager to install a JRE >= 11, " "or a user-local install via https://adoptium.net/ if you lack sudo.", - Network accessline 107
"Other distros: use your package manager to install a JRE >= 11, " "or a user-local install via https://adoptium.net/ if you lack sudo.", ) - Network accessline 108
"or a user-local install via https://adoptium.net/ if you lack sudo.",
skills/tooluniverse-nfcore-pipelines/scripts/detect_data_type.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-nfcore-pipelines/scripts/generate_samplesheet.py
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#!/usr/bin/env python3
skills/tooluniverse-nih-funding-landscape/agents/openai.yaml
skills/tooluniverse-nih-funding-landscape/references/public-value-cases.md
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type-7 transfer `7R01NS121038-02` in FY2022, and continued through `5R01NS121038-05` at UT Southwestern. These are five full project numbers but one distinct core award. `fetch("R01NS121038")` correctly failed because fetch - Network accessline 327
`5R01NS121038-05` at UT Southwestern. These are five full project numbers but one distinct core award. `fetch("R01NS121038")` correctly failed because fetch requires a full project number; either full endpoint record returned a public - Network accessline 328
one distinct core award. `fetch("R01NS121038")` correctly failed because fetch
skills/tooluniverse-nih-funding-landscape/references/tool-reference.md
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search_grants result.pi_profile_id -> get_pi_profile(profile_id) rank_institutions result.entity_id -> get_institution_profile / mechanism_mix / funding_growth search result.id OR search_grants result.project_num -> fetch(id) - Network accessline 47
rank_institutions result.entity_id -> get_institution_profile / mechanism_mix / funding_growth search result.id OR search_grants result.project_num -> fetch(id) search_grants result.project_num -> citation/fetch of one full record - Network accessline 48
search result.id OR search_grants result.project_num -> fetch(id) - Network accessline 289
than one row for one core in a fiscal year. Count the core once when answering “how many awards,” but do not discard legitimate row-level dollars. Fetch one of the full project numbers; `fetch(core_project_num)` returns an actionable - Network accessline 290
“how many awards,” but do not discard legitimate row-level dollars. Fetch one of the full project numbers; `fetch(core_project_num)` returns an actionable error because a core ID is not a citation record. - Network accessline 291
of the full project numbers; `fetch(core_project_num)` returns an actionable - Network accessline 315
1. Validate the full project-number form. 2. Run `search_grants(project_num=...)`. 3. If zero, run `fetch(id=...)` only as a canonical-record check; preserve its error message. - Network accessline 316
2. Run `search_grants(project_num=...)`. 3. If zero, run `fetch(id=...)` only as a canonical-record check; preserve its error message. 4. Read `source_status` and say “not found in this snapshot/query.” - Network accessline 317
3. If zero, run `fetch(id=...)` only as a canonical-record check; preserve its error message.
skills/tooluniverse-nih-funding-landscape/scripts/verify_live_cases.py
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#!/usr/bin/env python3 - Network accessline 769
"exact award fetch returns public URL", exact_fetch["id"] == "1F31DC023096-01" and exact_fetch["url"].startswith("https://opennih.org/projects/"), - Network accessline 770
exact_fetch["id"] == "1F31DC023096-01" and exact_fetch["url"].startswith("https://opennih.org/projects/"), exact_fetch, - Network accessline 771
and exact_fetch["url"].startswith("https://opennih.org/projects/"),
skills/tooluniverse-nih-funding-landscape/scripts/verify_live_contract.py
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#!/usr/bin/env python3
skills/tooluniverse-nih-funding-landscape/scripts/verify_output_links.py
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#!/usr/bin/env python3
skills/tooluniverse-noncoding-rna/SKILL.md
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# Step 1: Download TargetScan predicted targets (one-time, ~10MB zipped) # URL: https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip - Network accessline 234
# URL: https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip import pandas as pd import zipfile, io, requests - Network accessline 238
url = "https://www.targetscan.org/vert_80/vert_80_data_download/Summary_Counts.default_predictions.txt.zip" resp = requests.get(url, timeout=60) - Network accessline 239
resp = requests.get(url, timeout=60) - Network accessline 263
# Option 1: Download from miRTarBase bulk export (requires browser download first) # Go to: https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/ - Network accessline 264
# Go to: https://mirtarbase.cuhk.edu.cn/~miRTarBase/miRTarBase_2025/ - Network accessline 267
# Option 2: Use the GitHub data dump # https://github.com/jorainer/mirtarbase — R package with cached data - Network accessline 268
# https://github.com/jorainer/mirtarbase — R package with cached data
skills/tooluniverse-nutrition-food-composition/SKILL.md
skills/tooluniverse-organic-chemistry/SKILL.md
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skills/tooluniverse-organic-chemistry/scripts/chemistry_facts.py
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skills/tooluniverse-organic-chemistry/scripts/crystal_validator.py
skills/tooluniverse-organic-chemistry/scripts/degrees_of_unsaturation.py
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skills/tooluniverse-organic-chemistry/scripts/molecular_complexity.py
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#!/usr/bin/env python3
skills/tooluniverse-organic-chemistry/scripts/molecular_formula.py
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Executable file
skills/tooluniverse-organic-chemistry/scripts/smiles_verifier.py
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#!/usr/bin/env python3
skills/tooluniverse-organic-chemistry/scripts/stereochem_tracker.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-peptide-target-deorphanization/SKILL.md
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skills/tooluniverse-peptide-target-deorphanization/references/phases.md
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skills/tooluniverse-peptide-target-deorphanization/scripts/cofold_screen.py
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#!/usr/bin/env python3 - Access to secretsline 10
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skills/tooluniverse-peptide-target-deorphanization/scripts/deorphanize_peptide.py
skills/tooluniverse-pharmacogenomics/SKILL.md
skills/tooluniverse-pharmacokinetics/scripts/nca_from_csv.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-pharmacokinetics/test_nca_from_csv.py
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Executable file
skills/tooluniverse-pharmacovigilance/SKILL.md
- Network accessline 344
- FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers - Network accessline 345
- DailyMed: https://dailymed.nlm.nih.gov - Network accessline 346
- PharmGKB: https://www.pharmgkb.org - Network accessline 347
- ClinicalTrials.gov: https://clinicaltrials.gov - Network accessline 348
- OpenFDA: https://open.fda.gov - Network accessline 349
- KEGG Drug: https://www.genome.jp/kegg/drug
skills/tooluniverse-phylogenetics/SKILL.md
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`references/sequence_alignment.md`, `references/tree_building.md`, `references/parsimony_analysis.md`, `scripts/tree_statistics.py` - PhyKIT: https://jlsteenwyk.com/PhyKIT/ - Network accessline 647
- PhyKIT: https://jlsteenwyk.com/PhyKIT/ - Network accessline 648
- Biopython Phylo: https://biopython.org/wiki/Phylo - Network accessline 649
- DendroPy: https://dendropy.org/
skills/tooluniverse-phylogenetics/references/troubleshooting.md
- Network accessline 570
- `tree_building.md` - Tree construction methods - `parsimony_analysis.md` - Statistical workflows - PhyKIT documentation: https://jlsteenwyk.com/PhyKIT/ - Network accessline 571
- `parsimony_analysis.md` - Statistical workflows - PhyKIT documentation: https://jlsteenwyk.com/PhyKIT/ - Biopython Phylo tutorial: https://biopython.org/wiki/Phylo - Network accessline 572
- PhyKIT documentation: https://jlsteenwyk.com/PhyKIT/ - Network accessline 573
- Biopython Phylo tutorial: https://biopython.org/wiki/Phylo
skills/tooluniverse-phylogenetics/scripts/busco_target_orthologs.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-phylogenetics/scripts/format_alignment.py
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#!/usr/bin/env python3
skills/tooluniverse-phylogenetics/scripts/phykit_batch.py
skills/tooluniverse-phylogenetics/scripts/scogs_paired_compare.py
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#!/usr/bin/env python3
skills/tooluniverse-phylogenetics/scripts/scogs_phykit_pipeline.py
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#!/usr/bin/env python3
skills/tooluniverse-phylogenetics/scripts/tree_statistics.py
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#!/usr/bin/env python3
skills/tooluniverse-phylogenetics/test_phylogenetics.py
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#!/usr/bin/env python3
skills/tooluniverse-polygenic-risk-score/.env.template
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skills/tooluniverse-polygenic-risk-score/check_data.py
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skills/tooluniverse-polygenic-risk-score/python_implementation.py
skills/tooluniverse-polygenic-risk-score/test_prs_tools.py
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skills/tooluniverse-polygenic-risk-score/test_skill_comprehensive.py
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skills/tooluniverse-population-genetics/scripts/popgen_calculator.py
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#!/usr/bin/env python3
skills/tooluniverse-precision-medicine-stratification/.env.template
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skills/tooluniverse-precision-medicine-stratification/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-precision-oncology/scripts/gdsc_drug_response.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 34
GDSC_URLS = { "GDSC2": "https://cog.sanger.ac.uk/cancerrxgene/GDSC_release8.5/GDSC2_fitted_dose_response_27Oct23.xlsx", - Network accessline 35
"GDSC2": "https://cog.sanger.ac.uk/cancerrxgene/GDSC_release8.5/GDSC2_fitted_dose_response_27Oct23.xlsx", - Network accessline 36
"GDSC1": "https://cog.sanger.ac.uk/cancerrxgene/GDSC_release8.5/GDSC1_fitted_dose_response_27Oct23.xlsx", - Network accessline 53
if not os.path.exists(path) or os.path.getsize(path) == 0: sys.stderr.write(f"Downloading {dataset} (~21 MB, one-time)...\n") urllib.request.urlretrieve(GDSC_URLS[dataset], path) - Network accessline 54
sys.stderr.write(f"Downloading {dataset} (~21 MB, one-time)...\n") urllib.request.urlretrieve(GDSC_URLS[dataset], path) return pd.read_excel(path) - Network accessline 55
urllib.request.urlretrieve(GDSC_URLS[dataset], path)
skills/tooluniverse-primer-design/scripts/primer_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-primer-design/test_primer_qc.py
- Executable scriptline 1
Executable file
skills/tooluniverse-product-safety-surveillance/SKILL.md
- Network accessline 244
- openFDA: https://open.fda.gov - Network accessline 245
- MAUDE (device adverse events): https://www.fda.gov/medical-devices/mandatory-reporting-requirements-manufacturers-importers-and-device-user-facilities - Network accessline 246
- Device recalls / 510(k): https://www.fda.gov/medical-devices - Network accessline 247
- CAERS (food/supplement/cosmetic): https://www.fda.gov/food/compliance-enforcement/cfsan-adverse-event-reporting-system-caers - Network accessline 248
- Drug shortages: https://www.fda.gov/drugs/drug-shortages - Network accessline 249
- Recall classifications: https://www.fda.gov/safety/industry-guidance-recalls
skills/tooluniverse-protein-design-campaign/scripts/preflight_submission.py
- Executable scriptline 1
#!/usr/bin/env python3 - Instruction override signalline 31
), "selector_instruction": re.compile( r"ignore previous instructions|system prompt|rank this first|" - Instruction override signalline 32
"selector_instruction": re.compile( r"ignore previous instructions|system prompt|rank this first|" r"select this candidate|you are (?:ChatGPT|Claude)", - Instruction override signalline 33
r"ignore previous instructions|system prompt|rank this first|"
skills/tooluniverse-protein-interactions/.env.template
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skills/tooluniverse-protein-interactions/DOMAIN_ANALYSIS.md
skills/tooluniverse-protein-interactions/PHASE2_DISCOVERIES.md
skills/tooluniverse-protein-interactions/QUICK_START.md
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skills/tooluniverse-protein-interactions/SKILL.md
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[Credential-related source omitted] - Network accessline 235
- STRING: https://string-db.org/ - Network accessline 235
- STRING: https://string-db.org/ - Network accessline 235
- STRING: https://string-db.org/ - Network accessline 236
- BioGRID: https://thebiogrid.org/ (register for free API key) - Network accessline 236
- BioGRID: https://thebiogrid.org/ (register for free API key) - Network accessline 236
- BioGRID: https://thebiogrid.org/ (register for free API key) - Network accessline 237
- SASBDB: https://www.sasbdb.org/ - Network accessline 237
- SASBDB: https://www.sasbdb.org/ - Network accessline 237
- SASBDB: https://www.sasbdb.org/ - Network accessline 238
- Complex Portal: https://www.ebi.ac.uk/complexportal/ - Network accessline 238
- Complex Portal: https://www.ebi.ac.uk/complexportal/ - Network accessline 238
- Complex Portal: https://www.ebi.ac.uk/complexportal/ - Network accessline 239
- ToolUniverse: https://github.com/mims-harvard/ToolUniverse - Network accessline 239
- ToolUniverse: https://github.com/mims-harvard/ToolUniverse - Network accessline 239
- ToolUniverse: https://github.com/mims-harvard/ToolUniverse
skills/tooluniverse-protein-interactions/TOOL_DESCRIPTION_OPTIMIZATION.md
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"Query protein and genetic interactions from the BioGRID database. **Prerequisites**: Requires BIOGRID_API_KEY environment variable (request at https://webservice.thebiogrid.org/). Returns experimentally - Network accessline 34
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(request at https://webservice.thebiogrid.org/). Returns experimentally - Network accessline 94
- `species`: "NCBI taxonomy ID specifying organism. Common values: 9606 (Homo sapiens/human), 10090 (Mus musculus/mouse), 10116 (Rattus norvegicus/rat), 7227 (Drosophila melanogaster). Default 9606 (human). Find taxonomy IDs at https://www. - Network accessline 228
for Interaction Datasets). Returns curated interactions from published studies with evidence methods, PubMed citations, and throughput information. **Prerequisites**: Requires BIOGRID_API_KEY environment variable (free academic API key at h - Access to secretsline 229
[Credential-related source omitted] - Network accessline 230
variable (free academic API key at https://webservice.thebiogrid.org/). - Network accessline 267
ubiquitination, acetylation, methylation, and other covalent protein modifications with site positions, modifying enzymes, and literature evidence. **Prerequisites**: Requires BIOGRID_API_KEY environment variable (request at https://webserv - Access to secretsline 268
[Credential-related source omitted] - Network accessline 269
variable (request at https://webservice.thebiogrid.org/). - Access to secretsline 296
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**Parameter Improvements**: - `pubmed_ids`: "List of PubMed IDs (PMIDs) to query. **Note: plural parameter** - pass as list. Example: ['17200106'] (single paper) or ['17200106', '12345678'] (multiple papers). Returns all protein interaction - Network accessline 337
- `pubmed_ids`: "List of PubMed IDs (PMIDs) to query. **Note: plural parameter** - pass as list. Example: ['17200106'] (single paper) or ['17200106', '12345678'] (multiple papers). Returns all protein interactions curated from these publica - Network accessline 389
**Parameter Improvements**: - `sasbdb_id`: "SASBDB entry identifier. Format: 'SASDXXX' where XXX is alphanumeric (e.g., 'SASDAB7', 'SASD1P8'). Find IDs using SASBDB_search_entries or at https://www.sasbdb.org/. Example: 'SASDAB7' (p53 core - Network accessline 390
- `sasbdb_id`: "SASBDB entry identifier. Format: 'SASDXXX' where XXX is alphanumeric (e.g., 'SASDAB7', 'SASD1P8'). Find IDs using SASBDB_search_entries or at https://www.sasbdb.org/. Example: 'SASDAB7' (p53 core domain structure)." - Access to secretsline 439
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skills/tooluniverse-protein-interactions/python_implementation.py
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Executable file
skills/tooluniverse-protein-interactions/test_protein_tools.py
skills/tooluniverse-protein-interactions/test_skill_comprehensive.py
skills/tooluniverse-protein-lof-mechanism/SKILL.md
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- **esm package with SAE support**: `pip install 'esm @ git+https://github.com/evolutionaryscale/esm@ee891c52'` - Network accessline 256
git clone https://github.com/Kuhlman-Lab/ThermoMPNN.git - Network accessline 276
- [**Tamarind Bio**](https://www.tamarind.bio/tools/thermompnn) — REST API at `app.tamarind.bio/api/`, `x-api-key` auth - Network accessline 277
- [**Neurosnap**](https://neurosnap.ai/service/ThermoMPNN) — credit-based, HTTP API - Network accessline 278
- [**BioLM**](https://biolm.ai/models/thermompnn/) — `POST /api/v3/thermompnn/predict/`, Token auth - Network accessline 279
- [**ProteinIQ**](https://proteiniq.io/app/thermompnn) — web-first - Network accessline 280
- [**Levitate Bio**](https://support.levitate.bio/api/api-thermompnn/) — REST API
skills/tooluniverse-protein-modification-analysis/SKILL.md
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**Known broken (verified live, not a guess):** `EBIProteins_get_rna_editing` returns HTTP 404 directly from `https://www.ebi.ac.uk/proteins/api/rna_editing/...` for both of its own documented example accessions (P42262, P28335), while the s
skills/tooluniverse-protein-sae-variant-interpretation/SKILL.md
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pip install 'esm @ git+https://github.com/evolutionaryscale/esm@ee891c52' - Network accessline 51
**License note**: SAE outputs from Forge are governed by the [Cambrian Inference Clickthrough License](https://www.evolutionaryscale.ai/policies/cambrian-inference-clickthrough-license-agreement) — non-commercial / academic research use onl
skills/tooluniverse-protein-structure-prediction/SKILL.md
skills/tooluniverse-protein-structure-retrieval/examples.md
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# download_text_content (returns {"content", "size", "url", ...}). pdb_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 60
pdb_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" ) - Network accessline 61
url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 66
# Also get as mmCIF (modern format) cif_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 67
cif_file = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.cif" ) - Network accessline 68
url=f"https://files.rcsb.org/download/{pdb_id}.cif" - Network accessline 211
# Get all available formats from RCSB via download_text_content base = f"https://files.rcsb.org/download/{pdb_id}" - Network accessline 212
base = f"https://files.rcsb.org/download/{pdb_id}" - Network accessline 242
for pdb_id in candidates[:3]: structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id.upper()}.pdb" - Network accessline 243
structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id.upper()}.pdb" ) - Network accessline 244
url=f"https://files.rcsb.org/download/{pdb_id.upper()}.pdb"
skills/tooluniverse-protein-therapeutic-design/CHECKLIST.md
skills/tooluniverse-protein-therapeutic-design/DESIGN_PROCEDURES.md
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skills/tooluniverse-protein-therapeutic-design/SKILL.md
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skills/tooluniverse-protein-therapeutic-design/TOOLS_REFERENCE.md
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|------|---------|----------------| | `PDBeSIFTS_get_best_structures` | Find PDB structures for a UniProt accession (best first) | `uniprot_accession` | | `download_text_content` | Download a PDB file (no dedicated PDB-download tool exists) - Network accessline 159
| `PDBeSIFTS_get_best_structures` | Find PDB structures for a UniProt accession (best first) | `uniprot_accession` | | `download_text_content` | Download a PDB file (no dedicated PDB-download tool exists) | `url` (`https://files.rcsb.org/do - Network accessline 160
| `download_text_content` | Download a PDB file (no dedicated PDB-download tool exists) | `url` (`https://files.rcsb.org/download/<PDB_ID>.pdb`) | - Network accessline 169
pdb_id = structures[0]["pdb_id"].upper() structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 170
structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" )["content"] - Network accessline 171
url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 205
# Get the atomic model (PDB) for design structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Network accessline 206
structure = tu.tools.download_text_content( url=f"https://files.rcsb.org/download/{pdb_id}.pdb" )["content"] - Network accessline 207
url=f"https://files.rcsb.org/download/{pdb_id}.pdb" - Access to secretsline 422
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skills/tooluniverse-proteomics-analysis/SKILL.md
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**Databases**: [STRING](https://string-db.org), [PhosphoSitePlus](https://www.phosphosite.org), [CORUM](https://mips.helmholtz-muenchen.de/corum)
skills/tooluniverse-proteomics-data-retrieval/SKILL.md
skills/tooluniverse-publish-tool/SKILL.md
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- If you are connected to the hosted ToolUniverse (you have `list_remote_tools`), call it and look for the user's computer ("your computer ...") and the tool. - Otherwise ask the user whether https://connect.aiscientist.tools/remote-servers - Network accessline 17
- Otherwise ask the user whether https://connect.aiscientist.tools/remote-servers shows the computer **online** with the tool listed. - Network accessline 43
1. Open https://connect.aiscientist.tools/my-tools?create=1 (sign in if asked). The **Publish a tool** window opens. - Network accessline 54
- **Public**: the tool should appear in https://connect.aiscientist.tools/discover (Tools view, "Shared by scientists"). If you have `list_remote_tools`, search for it by name and run it once with the same example.
skills/tooluniverse-rare-disease-diagnosis/CHECKLIST.md
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skills/tooluniverse-rare-disease-diagnosis/DIAGNOSTIC_WORKFLOW.md
- Access to secretsline 493
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skills/tooluniverse-rare-disease-diagnosis/TOOLS_REFERENCE.md
- Access to secretsline 63
[Credential-related source omitted] - Access to secretsline 99
[Credential-related source omitted] - Network accessline 323
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skills/tooluniverse-rare-disease-diagnosis/scripts/clinical_patterns.py
skills/tooluniverse-regulatory-genomics/SKILL.md
skills/tooluniverse-regulatory-variant-analysis/SKILL.md
skills/tooluniverse-rnaseq-deseq2/.env.template
- Access to secretsline 2
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skills/tooluniverse-rnaseq-deseq2/scripts/convert_rds_to_csv.py
- Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 177
print(" macOS: brew install r") print(" Ubuntu: sudo apt-get install r-base") print(" Or visit: https://cran.r-project.org/") - Dangerous shell operationline 178
print(" Ubuntu: sudo apt-get install r-base") - Network accessline 178
print(" Ubuntu: sudo apt-get install r-base") print(" Or visit: https://cran.r-project.org/") sys.exit(1) - Network accessline 179
print(" Or visit: https://cran.r-project.org/")
skills/tooluniverse-rnaseq-deseq2/scripts/format_deseq2_output.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-rnaseq-deseq2/scripts/gene_length_correlation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-rnaseq-deseq2/scripts/load_count_matrix.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-rnaseq-deseq2/scripts/multi_strain_venn.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-rnaseq-deseq2/scripts/one_way_anova_f.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-rnaseq-deseq2/scripts/pca_variance.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-rnaseq-deseq2/scripts/r_deseq2_wrapper.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-rnaseq-deseq2/scripts/r_edger_limma_wrapper.py
skills/tooluniverse-rnaseq-deseq2/test_skill.py
skills/tooluniverse-scvi-tools/scripts/_scvi_common.py
- Executable scriptline 1
Executable file
skills/tooluniverse-scvi-tools/scripts/differential_expression.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-scvi-tools/scripts/prepare_data.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-scvi-tools/scripts/train_model.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-scvi-tools/scripts/transfer_labels.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-scvi-tools/scripts/validate_adata.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-sdk/REFERENCE.md
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client = ToolUniverseClient("http://server:8080") - Access to secretsline 386
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[Credential-related source omitted] - Network accessline 497
- **Main Documentation**: https://zitniklab.hms.harvard.edu/ToolUniverse/ - Network accessline 498
- **API Reference**: https://zitniklab.hms.harvard.edu/ToolUniverse/api/modules.html - Network accessline 499
- **Tutorials**: https://zitniklab.hms.harvard.edu/ToolUniverse/guide/scientific_workflows.html - Network accessline 500
- **GitHub Examples**: https://github.com/mims-harvard/ToolUniverse/tree/main/examples - Network accessline 501
- **Community Slack**: https://join.slack.com/t/tooluniversehq/shared_invite/zt-3dic3eoio-5xxoJch7TLNibNQn5_AREQ
skills/tooluniverse-sdk/SKILL.md
skills/tooluniverse-self-review/references/ret-scored-evaluation.md
skills/tooluniverse-sequence-analysis/scripts/amino_acids.py
skills/tooluniverse-sequence-analysis/scripts/biology_facts.py
skills/tooluniverse-sequence-analysis/scripts/sequence_tools.py
- Executable scriptline 1
#!/usr/bin/env python3 - Executable scriptline 1
#!/usr/bin/env python3 - Executable scriptline 1
#!/usr/bin/env python3 - Network accessline 260
url = f"https://rest.uniprot.org/uniprotkb/{accession}.fasta" try: with urllib.request.urlopen(url, timeout=15) as resp: - Network accessline 260
url = f"https://rest.uniprot.org/uniprotkb/{accession}.fasta" try: with urllib.request.urlopen(url, timeout=15) as resp: - Network accessline 260
url = f"https://rest.uniprot.org/uniprotkb/{accession}.fasta" try: with urllib.request.urlopen(url, timeout=15) as resp: - Network accessline 261
try: with urllib.request.urlopen(url, timeout=15) as resp: fasta = resp.read().decode("utf-8") - Network accessline 261
try: with urllib.request.urlopen(url, timeout=15) as resp: fasta = resp.read().decode("utf-8") - Network accessline 261
try: with urllib.request.urlopen(url, timeout=15) as resp: fasta = resp.read().decode("utf-8") - Network accessline 262
with urllib.request.urlopen(url, timeout=15) as resp: - Network accessline 262
with urllib.request.urlopen(url, timeout=15) as resp: - Network accessline 262
with urllib.request.urlopen(url, timeout=15) as resp:
skills/tooluniverse-sequence-analysis/scripts/translate_dna.py
skills/tooluniverse-share-computer/SKILL.md
- Dangerous shell operationline 36
curl -LsSf https://connect.aiscientist.tools/install.sh | sh - Network accessline 36
curl -LsSf https://connect.aiscientist.tools/install.sh | sh - Network accessline 42
powershell -ExecutionPolicy ByPass -c "irm https://connect.aiscientist.tools/install.ps1 | iex" - Network accessline 50
python -m pip install -r https://connect.aiscientist.tools/install-requirements.txt - Network accessline 88
If they already run an MCP server (Streamable HTTP, for example `http://localhost:8080/mcp`), share it unchanged with `--forward` in step 4. Confirm it answers MCP `tools/list` first; a web page or REST API is not MCP, so wrap it as in path - Network accessline 117
Authorize this computer in your browser: https://connect.aiscientist.tools/activate?user_code=ABCD-EFGH - Network accessline 118
https://connect.aiscientist.tools/activate?user_code=ABCD-EFGH - Network accessline 133
# A. own code ~/.local/bin/tu serve share_tool.py --share --detach --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 134
~/.local/bin/tu serve share_tool.py --share --detach --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 135
# B. existing MCP program ~/.local/bin/tu serve --forward "http://localhost:8080" --share --detach --name "Smith Lab Server" --service https://tooluniverse-backend.onrender.com # C. reviewed models - Network accessline 136
~/.local/bin/tu serve --forward "http://localhost:8080" --share --detach --name "Smith Lab Server" --service https://tooluniverse-backend.onrender.com - Network accessline 138
~/.local/bin/tu serve --allow esm,boltz --share --detach --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 149
- If you are connected to the hosted ToolUniverse (you have `list_remote_tools`), call `list_remote_tools` with the computer's name, then `run_remote_tool` once with the same example you tested in step 3. Compare the answer with the local r - Network accessline 150
- Otherwise ask the user to open https://connect.aiscientist.tools/remote-servers and confirm the computer shows as online with the expected tools. - Network accessline 157
~/.local/bin/tuplatform-service install --tool-file "$PWD/share_tool.py" --name "Smith Lab GPU" --service https://tooluniverse-backend.onrender.com - Network accessline 166
- **Specific colleagues use this computer's tools**: on https://connect.aiscientist.tools/remote-servers, the computer's card has **Create share code**. The user copies the code (or **Copy invitation**) and sends it. A colleague pastes it o
skills/tooluniverse-single-cell/.env.template
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skills/tooluniverse-single-cell/scripts/find_markers.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-single-cell/scripts/normalize_data.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-single-cell/scripts/qc_metrics.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-single-cell/scripts/scrna_qc.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-single-cell/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-spatial-omics-analysis/test_spatial_omics.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-spatial-transcriptomics/SKILL.md
- Network accessline 316
geo_id = "GSE123456" # Check for h5ad or MTX in supplementary files url = f"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={geo_id}&targ=gsm&view=data" - Network accessline 318
url = f"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc={geo_id}&targ=gsm&view=data" - Network accessline 323
# HuBMAP data portal # Search at https://portal.hubmapconsortium.org/search then download via globus or direct link - Network accessline 324
# Search at https://portal.hubmapconsortium.org/search then download via globus or direct link - Network accessline 325
# Human Cell Atlas: https://data.humancellatlas.org/ — download h5ad/loom files - Network accessline 345
**Methods**: - Squidpy: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 346
- Squidpy: https://doi.org/10.1038/s41592-021-01358-2 - Network accessline 347
- Cell2location: https://doi.org/10.1038/s41587-021-01139-4 - Network accessline 348
- SpatialDE: https://doi.org/10.1038/nmeth.4636 - Network accessline 350
**Platforms**: - 10x Visium: https://www.10xgenomics.com/products/spatial-gene-expression - Network accessline 351
- 10x Visium: https://www.10xgenomics.com/products/spatial-gene-expression - Network accessline 352
- MERFISH: https://doi.org/10.1126/science.aaa6090 - Network accessline 353
- Slide-seq: https://doi.org/10.1126/science.aaw1219
skills/tooluniverse-statistical-modeling/SKILL.md
skills/tooluniverse-statistical-modeling/scripts/expression_anova.py
skills/tooluniverse-statistical-modeling/scripts/format_statistical_output.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-statistical-modeling/scripts/logistic_regression_or.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-statistical-modeling/scripts/model_diagnostics.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-statistical-modeling/scripts/power_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-statistical-modeling/scripts/prepare_ae_cohort.py
skills/tooluniverse-statistical-modeling/scripts/r_natural_spline_regression.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-statistical-modeling/scripts/sdtm_ordinal_logistic.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-statistical-modeling/scripts/spline_model_compare.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-statistical-modeling/scripts/stat_tests.py
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Executable file
skills/tooluniverse-statistical-modeling/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-structural-proteomics/SKILL.md
- Network accessline 311
- PDBePISA: `operation` is internal, not a public parameter - BMRB: `BMRB_get_entries_by_pdb_id` is a BLAST-based sequence match, not a curated 1:1 PDB<->BMRB cross-reference -- a query can return several candidate entries (or none) for a r - Network accessline 312
- BMRB: `BMRB_get_entries_by_pdb_id` is a BLAST-based sequence match, not a curated 1:1 PDB<->BMRB cross-reference -- a query can return several candidate entries (or none) for a real NMR PDB ID; `BMRB_search_chemical_shifts` is slow (15-20 - Network accessline 313
- MobiDB: `MobiDB_get_protein` and `MobiDB_get_consensus` were verified live (repeatedly, with a 90s timeout) to hang indefinitely against their configured endpoint (`https://mobidb.org/api/download`) -- confirmed independently via direct `
skills/tooluniverse-structural-variant-analysis/ANALYSIS_PROCEDURES.md
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# Note: ToolUniverse has no DECIPHER tool - look up similar patient cases # manually in the DECIPHER web portal (https://www.deciphergenomics.org). - Network accessline 131
# manually in the DECIPHER web portal (https://www.deciphergenomics.org). - Network accessline 216
# Note: no DECIPHER tool exists in ToolUniverse - check DECIPHER cohorts # for these genes manually at https://www.deciphergenomics.org. - Network accessline 217
# for these genes manually at https://www.deciphergenomics.org.
skills/tooluniverse-structural-variant-analysis/SKILL.md
- Network accessline 245
- ClinGen Dosage Sensitivity Map: https://www.ncbi.nlm.nih.gov/projects/dbvar/clingen/
skills/tooluniverse-synthetic-biology-parts/SKILL.md
- Network accessline 31
$ tu run SynBioHub_get_part '{"display_id": "BBa_E0040"}' Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely - Network accessline 32
Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely an HTML login or error page). The part may not exist, or SynBioHub may now - Network accessline 33
'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely - Network accessline 42
1. Tell the user plainly: SynBioHub now requires authentication for these reads; this is an upstream policy change, not a bug in the request. 2. Point them to create a free account at https://synbiohub.org, log in, - Network accessline 43
reads; this is an upstream policy change, not a bug in the request. 2. Point them to create a free account at https://synbiohub.org, log in, and retrieve a session token, then set `SYNBIOHUB_API_TOKEN` in the - Network accessline 44
2. Point them to create a free account at https://synbiohub.org, log in, - Access to secretsline 71
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deliberately hides any tool with unmet `required_api_keys` until the key is present — verified, not a bug); register for a free key at https://developers.addgene.org/ if it's missing. - Network accessline 73
present — verified, not a bug); register for a free key at https://developers.addgene.org/ if it's missing. - Cloning workflow steps (restriction sites, Gibson/Golden Gate assembly - Network accessline 74
https://developers.addgene.org/ if it's missing.
skills/tooluniverse-synthetic-biology-parts/references/synbiohub_tool_reference.md
- Network accessline 32
$ tu run SynBioHub_get_part '{"display_id": "BBa_E0040"}' Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, - Network accessline 33
Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely an HTML login or error page). The part may not exist, or - Network accessline 34
'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, - Network accessline 38
$ tu run SynBioHub_get_part '{"part_uri": "https://synbiohub.org/public/igem/BBa_E0040/1"}' - Network accessline 39
Error: SynBioHub did not return SBOL/XML data for 'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, likely an HTML login or error page). The part may not exist, or - Network accessline 40
'https://synbiohub.org/public/igem/BBa_E0040/1' (got non-XML content, - Network accessline 84
- **Parameters:** exactly one of `display_id` (string, e.g. `BBa_E0040` for iGEM BioBricks) or `part_uri` (string, full SynBioHub URI, e.g. `https://synbiohub.org/public/igem/BBa_E0040/1` — typically taken from - Network accessline 85
for iGEM BioBricks) or `part_uri` (string, full SynBioHub URI, e.g. `https://synbiohub.org/public/igem/BBa_E0040/1` — typically taken from a search result's `uri` field). Both are individually optional/nullable - Network accessline 86
`https://synbiohub.org/public/igem/BBa_E0040/1` — typically taken from - Access to secretsline 105
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https://developers.addgene.org/) to use Addgene lookups alongside this
skills/tooluniverse-systems-biology/.env.template
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skills/tooluniverse-systems-biology/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-systems-biology/test_skill.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-target-research/IMPLEMENTATION.md
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skills/tooluniverse-target-research/REFERENCE.md
skills/tooluniverse-target-research/SKILL.md
- Access to secretsline 248
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skills/tooluniverse-toxicology/SKILL.md
- Access to secretsline 240
[Credential-related source omitted] - Access to secretsline 361
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- AOPWiki: https://aopwiki.org - Network accessline 371
- FAERS: https://www.fda.gov/drugs/questions-and-answers-fdas-adverse-event-reporting-system-faers - Network accessline 372
- CTD: http://ctdbase.org - Network accessline 373
- DailyMed: https://dailymed.nlm.nih.gov - Network accessline 374
- OpenFDA: https://open.fda.gov - Network accessline 375
- EPA CompTox: https://comptox.epa.gov/dashboard/ (API docs: https://comptox.epa.gov/ctx-api/docs/)
skills/tooluniverse-use-shared-computer/SKILL.md
- Network accessline 14
1. Open https://connect.aiscientist.tools/dashboard (sign in if asked). - Network accessline 25
- Call `list_remote_tools` (optionally with words from the task, such as "protein" or the computer's name). It lists tools on the user's own computers, computers they joined, and published tools, each with an id, a description, its inputs a - Network accessline 26
- Published tools can also be browsed at https://connect.aiscientist.tools/discover ("Shared by scientists"). - Network accessline 41
The user can leave a shared computer on https://connect.aiscientist.tools/remote-servers: its card has **Stop using**. The owner can also turn the code off at any time.
skills/tooluniverse-vaccine-design/scripts/population_coverage.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-variant-analysis/.env.template
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skills/tooluniverse-variant-analysis/python_implementation.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-variant-analysis/scripts/annotate_variants.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-variant-analysis/scripts/coding_variant_filter.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-variant-analysis/scripts/filter_variants.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-variant-analysis/scripts/gatk_haplotypecaller_pipeline.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-variant-analysis/scripts/parse_vcf.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-variant-analysis/scripts/variant_fraction.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-variant-analysis/test_variant_analysis.py
- Executable scriptline 1
#!/usr/bin/env python3
skills/tooluniverse-variant-interpretation/CHECKLIST.md
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skills/tooluniverse-variant-interpretation/SKILL.md
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skills/tooluniverse-variant-interpretation/TOOLS_REFERENCE.md
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skills/tooluniverse-variant-predictor-dms-validation/SKILL.md
- Network accessline 182
# The official DeepMind release contains per-variant continuous scores # (not just bin assignments). Not currently wrapped by a TU tool — fetch directly: # https://alphafold.ebi.ac.uk/files/AF-<uniprot_id>-F1-aa-substitutions.csv - Network accessline 183
# (not just bin assignments). Not currently wrapped by a TU tool — fetch directly: # https://alphafold.ebi.ac.uk/files/AF-<uniprot_id>-F1-aa-substitutions.csv # or stream-filter https://storage.googleapis.com/dm_alphamissense/AlphaMissens - Network accessline 184
# https://alphafold.ebi.ac.uk/files/AF-<uniprot_id>-F1-aa-substitutions.csv - Network accessline 185
# or stream-filter https://storage.googleapis.com/dm_alphamissense/AlphaMissense_aa_substitutions.tsv.gz - Access to secretsline 206
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skills/tooluniverse-variant-to-mechanism/SKILL.md
- Access to secretsline 258
[Credential-related source omitted] - Network accessline 350
# Ensembl VEP REST — functional consequence prediction vep = requests.get(f"https://rest.ensembl.org/vep/human/id/{rsid}?content-type=application/json").json() - Network accessline 351
vep = requests.get(f"https://rest.ensembl.org/vep/human/id/{rsid}?content-type=application/json").json() - Network accessline 353
# GTEx eQTL — tissue-specific expression effects eqtl = requests.get(f"https://gtexportal.org/api/v2/association/singleTissueEqtl", - Network accessline 354
eqtl = requests.get(f"https://gtexportal.org/api/v2/association/singleTissueEqtl", - Network accessline 357
# ClinVar bulk (variant_summary.txt.gz, ~200MB, all variants) # Download once, filter locally: # df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") - Network accessline 358
# Download once, filter locally: # df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t") # hits = df[df["RS# (dbSNP)"] == int(rsid.replace("rs",""))] - Network accessline 359
# df = pd.read_csv("https://ftp.ncbi.nlm.nih.gov/pub/clinvar/tab_delimited/variant_summary.txt.gz", sep="\t")
skills/tooluniverse/.env.template
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[Credential-related source omitted] - Access to secretsline 22
[Credential-related source omitted]
skills/tooluniverse/REFERENCE.md
- Access to secretsline 139
[Credential-related source omitted]
How the signals fit together
Facts from a static read of the files at the pinned commit. They describe what the code does, not why.
Some files could not be read; the facts below still hold.
Combined signals: 1 fact
- Runs the official installer from astral.shsetup-tooluniverse/SKILL.md:51Low confidence
Signal confidence: 499 facts
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:9Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:14Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:15Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:16Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:17Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:21Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:23Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:28Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:29Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:30Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:35Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:38Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:43Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:44Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:45Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:49Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:51Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:56Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:57Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:58Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:63Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:70Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:75Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:76Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:77Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:81Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:89Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:90Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:91Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:96Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:99Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:104Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:105Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:106Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:110Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:113Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:118Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:119Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:120Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:121Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:124Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:126Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:131Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:132Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:133Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:137Low confidence
- Access to secrets in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:139Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:144Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:145Low confidence
- Network access in prose of documentationsetup-tooluniverse/API_KEYS_REFERENCE.md:146Low confidence
and 449 more
Only the first 500 facts are listed.
Works with
| Agent | Status | Evidence | Install to |
|---|---|---|---|
| Claude Code | Native | Manifest .claude-plugin/marketplace.json | .claude/skills/<skill-name>/ |
| Codex | Compatible | Standard SKILL.md plugin/skills/setup-tooluniverse/SKILL.md | .agents/skills/<skill-name>/ |
| Cursor | Compatible | Standard SKILL.md plugin/skills/setup-tooluniverse/SKILL.md | .cursor/skills/<skill-name>/ |
| Gemini CLI | Compatible | Standard SKILL.md plugin/skills/setup-tooluniverse/SKILL.md | .gemini/skills/<skill-name>/ |
| GitHub Copilot | Compatible | Standard SKILL.md plugin/skills/setup-tooluniverse/SKILL.md | .github/skills/<skill-name>/ |
| OpenCode | Compatible | Standard SKILL.md plugin/skills/setup-tooluniverse/SKILL.md | .opencode/skills/<skill-name>/ |
- Native The repository ships a manifest, folder or frontmatter field for this agent.
- Compatible A standard SKILL.md that this agent can read.
- Unknown No evidence either way.
Derived automatically from paths, manifests and frontmatter. Not tested on each agent.
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