Systematic diagnosis support for rare diseases using phenotype matching, gene panel prioritization, and variant interpretation across Orphanet, OMIM, HPO, ClinVar, and structure-based analysis.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontooluniverse-rare-disease-diagnosisExecute the skills CLI command in your project's root directory to begin installation:
Fetches tooluniverse-rare-disease-diagnosis from mims-harvard/tooluniverse and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate tooluniverse-rare-disease-diagnosis. Access via /tooluniverse-rare-disease-diagnosis in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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:
When uncertain about any scientific fact, SEARCH databases first rather than reasoning from memory.
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 these strategies to form a 3-5 candidate differential, then use tools to confirm/refute:
Common pitfalls: Felty's (RA+splenomegaly+neutropenia) mimics infection; SLE nephritis mimics PSGN (check ASO); occupational exposures trigger autoimmunity (silica→scleroderma/RA/SLE).
| Tool | WRONG | CORRECT |
|---|---|---|
OpenTargets_get_associated_drugs_by_target_ensemblID |
ensemblID |
ensemblId |
ClinVar_get_variant_details |
variant_id |
id |
MyGene_query_genes |
gene |
q |
gnomad_get_variant |
variant |
variant_id |
Phase 0: Clinical Reasoning → 3-5 candidate differential
Phase 1: Phenotype → HPO terms (HPO_search_terms), core vs variable, onset, family history
Phase 2: Disease Matching → Orphanet_search_diseases, OMIM_search, DisGeNET_search_gene
Phase 3: Gene Panel → ClinGen validation, GTEx expression, prioritization scoring
Phase 3.5: Expression Context → CELLxGENE, ChIPAtlas for tissue/cell-type confirmation
Phase 3.6: Pathway Analysis → KEGG, IntAct for convergent pathways
Phase 4: Variant Interpretation → ClinVar, gnomAD frequency, CADD/AlphaMissense/EVE/SpliceAI, ACMG criteria
Phase 5: Structure Analysis → AlphaFold2, InterPro domains (for VUS)
Phase 6: Literature → PubMed, BioRxiv/MedRxiv, OpenAlex
Phase 7: Report Synthesis → Prioritized differential with next steps
Phase 2 - Disease Matching: Orphanet_search_diseases(operation="search_diseases", query=keyword) then Orphanet_get_genes(operation="get_genes", orpha_code=code). Score overlap: Excellent >80%, Good 60-80%, Possible 40-60%.
Phase 3 - Gene Panel: ClinGen classification drives inclusion (Definitive/Strong/Moderate = include; Limited = flag; Disputed/Refuted = exclude). Scoring: Tier 1 (top disease gene +5), Tier 2 (multi-disease +3), Tier 3 (ClinGen Definitive +3), Tier 4 (tissue expression +2), Tier 5 (pLI >0.9 +1).
Phase 4 - Variants: gnomAD frequency classes: ultra-rare <0.00001, rare <0.0001, low-freq <0.01. ACMG: PVS1 (null), PS1 (same AA), PM2 (absent pop), PP3 (computational), BA1 (>5% AF). 2+ concordant predictors strengthen PP3.
| Tier | Criteria |
|---|---|
| T1 (High) | Phenotype match >80% + gene match |
| T2 (Medium-High) | Phenotype match 60-80% OR likely pathogenic variant |
| T3 (Medium) | Phenotype match 40-60% OR VUS in candidate gene |
| T4 (Low) | Phenotype <40% OR uncertain gene |
| Primary | Fallback 1 | Fallback 2 |
|---|---|---|
get_joint_associated_diseases_by_HPO_ID_list |
Orphanet_search_diseases |
PubMed phenotype search |
ClinVar_get_variant_details |
gnomad_get_variant |
VEP annotation |
GTEx_get_expression_summary |
HPA_search_genes_by_query |
Tissue-specific literature |
scripts/clinical_patterns.py - Clinical pattern lookup (syndromes, differentials, red flags, occupational exposures)Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
tooluniverse-rare-disease-diagnosis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Useful defaults in tooluniverse-rare-disease-diagnosis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
We added tooluniverse-rare-disease-diagnosis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Registry listing for tooluniverse-rare-disease-diagnosis matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: tooluniverse-rare-disease-diagnosis is the kind of skill you can hand to a new teammate without a long onboarding doc.
tooluniverse-rare-disease-diagnosis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
I recommend tooluniverse-rare-disease-diagnosis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tooluniverse-rare-disease-diagnosis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
tooluniverse-rare-disease-diagnosis reduced setup friction for our internal harness; good balance of opinion and flexibility.
tooluniverse-rare-disease-diagnosis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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