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Tooluniverse Rare Disease Diagnosis

This is a structured clinical reasoning system for rare disease diagnosis that converts patient symptoms into HPO terms, then searches across Orphanet, OMIM, and ClinVar to generate ranked differential diagnoses. It walks through phenotype matching, gene panel prioritization with ClinGen validation, and ACMG variant interpretation with computational predictors. The workflow is opinionated: it creates a report file first and updates it progressively, insists on searching databases rather than guessing, and emphasizes multi-system involvement and treatable conditions. Useful when you're building diagnostic support tools or working with genetic counseling data. The clinical reasoning framework is solid, with practical heuristics like looking for regression patterns and occupational exposures that can have 50-year latencies.

skills.sh installs
410
GitHub stars
1.8K
last updated
Aug 21, 2026

Install

$ npx -y skills add mims-harvard/tooluniverse --skill tooluniverse-rare-disease-diagnosis --agent claude-code

Installs into .claude/skills of the current project.

Files

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.


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.

Clinical Reasoning Framework (BEFORE Tools)

Sponsors

Details

Repository
github.com/mims-harvard/tooluniverse
Category
Marketing & SEO
First seen
Jun 3, 2026

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