Tooluniverse Structural Variant Analysis

This handles clinical interpretation of structural variants like deletions, duplications, and inversions using ACMG-adapted criteria. It walks through a seven-phase workflow: normalizing coordinates, identifying affected genes, pulling ClinGen haploinsufficiency and triplosensitivity scores, checking gnomAD population frequencies, and scoring pathogenicity on a 0-10 scale before mapping to the five-tier clinical classification. The dosage sensitivity logic is explicit: a deletion is only pathogenic if it removes a haploinsufficient gene, a duplication matters when the gene is triplosensitive. It enforces a compute-first approach, running Python for scoring rather than describing what you'd do. Use this when you need structured evidence grading for CNV or chromosomal rearrangement reports.

skills.sh installs: 374
GitHub stars: 1.8K
last updated: Aug 21, 2026

View on GitHub

Install

$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-structural-variant-analysis --agent claude-code

Installs into .claude/skills of the current project.

Files

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.

SKILL.md on GitHub