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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.
Passed 10 of 15 test cases with the skill, and 5 of 15 without it.
and 6 more in the full report
9 fixed5 passed either way5 still failing3 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 9 test cases the skill fixed. Same model, same prompt, with and without the skill.
When retrieving gene metadata via MyGene_query_genes, what parameter name carries the search term string?
| Checked | Without skill | With skill |
|---|---|---|
| Uses query as the search term parameter name for MyGene_query_genes. | Missed | Passed |
The instructions your model reads when the skill loads.
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:
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.
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.
Apply when user asks:
Required: Gene symbol + variant notation (e.g., "EGFR L858R", "BRAF p.V600E", "EML4-ALK fusion", "HER2 amplification") Optional: Cancer type (improves specificity)
test cases passed, vs 5 / 15 without
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