tooluniverse▌

3 calling patterns -- start with pattern 1:

Retrieve protein structures with disambiguation, quality assessment, and comprehensive metadata.

Retrieve comprehensive chemical compound data with proper disambiguation and cross-database validation.

Generate a comprehensive disease research report with full source citations. The report is created as a markdown file and progressively updated during research.

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

Systematic literature research: disambiguate, search with collision-aware queries, grade evidence, produce structured reports.

Production-ready skill for analyzing microscopy-derived measurement data using pandas, numpy, scipy, statsmodels, and scikit-image.

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.

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.

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.