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.
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiontooluniverse-structural-variant-analysisExecute the skills CLI command in your project's root directory to begin installation:
Fetches tooluniverse-structural-variant-analysis 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-structural-variant-analysis. Access via /tooluniverse-structural-variant-analysis in your agent's command palette.
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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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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.
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.
KEY PRINCIPLES:
Use this skill when users:
Before any tool call, apply this reasoning to frame the analysis:
SV pathogenicity depends on what the SV disrupts. A deletion removing an entire gene is likely pathogenic if the gene is haploinsufficient. A duplication is pathogenic if the gene is dosage-sensitive. An inversion is pathogenic only if it disrupts a coding region or regulatory element at the breakpoint.
Work through these questions in order:
1. What type is the SV, and what disruption mechanism does it cause?
2. Is the disrupted gene dosage-sensitive?
3. Does the population frequency contextualize the SV?
=1% frequency in gnomAD SV = BA1 (likely benign unless phenotype is extreme)
4. Is there clinical precedent?
Document this reasoning before computing the final score.
Phase 1: SV IDENTITY & CLASSIFICATION
Normalize coordinates (hg19/hg38), determine type (DEL/DUP/INV/TRA/CPX),
calculate size, assess breakpoint precision
Phase 2: GENE CONTENT ANALYSIS
Identify fully contained genes, partially disrupted genes (breakpoint within),
flanking genes (within 1 Mb), annotate function and disease associations
Phase 3: DOSAGE SENSITIVITY ASSESSMENT
ClinGen HI/TS scores, pLI scores, OMIM inheritance patterns,
gene-disease validity levels
Phase 4: POPULATION FREQUENCY CONTEXT
gnomAD SV database, ClinVar known SVs, DECIPHER patient cases,
reciprocal overlap calculation (>=70% = same SV)
Phase 5: PATHOGENICITY SCORING
Quantitative 0-10 scale: gene content (40%), dosage sensitivity (30%),
population frequency (20%), clinical evidence (10%)
Phase 6: LITERATURE & CLINICAL EVIDENCE
PubMed searches, DECIPHER cohort analysis, functional evidence
Phase 7: ACMG-ADAPTED CLASSIFICATION
Apply SV-specific evidence codes, calculate final classification,
generate clinical recommendations
Goal: Standardize SV notation and classify type.
Capture: chromosome(s), coordinates (start/end in hg19/hg38), SV size, SV type (DEL/DUP/INV/TRA/CPX), breakpoint precision, inheritance pattern (de novo/inherited/unknown).
For SV type definitions, scoring tables, and ACMG code details, see CLASSIFICATION_GUIDE.md.
Goal: Annotate all genes affected by the SV.
Tools:
ensembl_lookup_gene - gene structure, coordinates, exonsNCBIGene_search - official symbol, aliases, descriptionGene_Ontology_get_term_info - biological process, molecular functionOMIM_search, OMIM_get_entry - disease associations, inheritanceDisGeNET_search_gene - gene-disease association scoresClassify genes as: fully contained (entire gene in SV), partially disrupted (breakpoint within gene), or flanking (within 1 Mb of breakpoints).
For implementation pseudocode, see ANALYSIS_PROCEDURES.md Phase 2.
Goal: Determine if affected genes are dosage-sensitive.
Tools:
ClinGen_search_dosage_sensitivity - HI/TS scores (0-3, gold standard)ClinGen_search_gene_validity - gene-disease validity levelgnomad_search_variants - pLI scores for LoF intoleranceOMIM_get_entry - inheritance pattern (AD suggests dosage sensitivity)Interpret scores using the reasoning above. ClinGen HI/TS score 3 = definitive; score 2 = likely; score 1 = little evidence; score 0 = no evidence. Do not equate AD inheritance with haploinsufficiency without ClinGen support.
Goal: Determine if SV is common (likely benign) or rare (supports pathogenicity).
Tools:
gnomad_search_variants - population SV frequenciesClinVar_search_variants - known pathogenic/benign SVsClinGen_search_dosage_sensitivity - patient SVs with phenotypesUse >=70% reciprocal overlap to define "same" SV for comparison. A frequency >=1% triggers BA1 unless there is very strong clinical evidence to override.
Goal: Quantitative pathogenicity assessment on 0-10 scale.
Four components weighted: gene content (40%), dosage sensitivity (30%), population frequency (20%), clinical evidence (10%).
Score mapping: 9-10 = Pathogenic, 7-8 = Likely Pathogenic, 4-6 = VUS, 2-3 = Likely Benign, 0-1 = Benign.
For detailed scoring breakdowns and implementation, see CLASSIFICATION_GUIDE.md and ANALYSIS_PROCEDURES.md Phase 5.
Goal: Find case reports, functional studies, and clinical validation.
Tools:
PubMed_search_articles - peer-reviewed literatureEuropePMC_search_articles - additional coverageClinGen_search_dosage_sensitivity - patient case databaseSearch strategies: gene-specific dosage sensitivity papers, SV-specific case reports, phenotype-gene associations. See ANALYSIS_PROCEDURES.md Phase 6.
Goal: Apply ACMG/ClinGen criteria adapted for SVs and generate a final classification with explicit evidence summary.
The LLM knows the ACMG criteria codes and combination rules. Apply them to the evidence gathered in Phases 1-6. Key points to verify with tool data:
For complete evidence code tables and classification algorithm, see CLASSIFICATION_GUIDE.md.
Create report using the template in REPORT_TEMPLATE.md. Name files as:
SV_analysis_[TYPE]_chr[CHR]_[START]_[END]_[GENES].md
ClinGen_search_dosage_sensitivity - HI/TS scores (required for all deletions/duplications)ClinGen_search_gene_validity - gene-disease validity (required)ClinVar_search_variants - known pathogenic/benign SVs (required)ensembl_lookup_gene - gene coordinates, structure (required)OMIM_search, OMIM_get_entry - gene-disease associations (required)gnomad_search_variants - population frequency and pLI (required)DisGeNET_search_gene - additional disease associations (recommended)PubMed_search_articles - literature evidence (recommended)Gene_Ontology_get_term_info - gene function (supporting)tooluniverse-variant-interpretationUse this skill for structural variants >=50 bp requiring dosage sensitivity assessment and ACMG-adapted classification.
EXAMPLES.md - Sample SV interpretations with worked examplesCLASSIFICATION_GUIDE.md - ACMG criteria, scoring system, evidence codes, special scenarios, clinical recommendationsREPORT_TEMPLATE.md - Full report template with section structure and file namingANALYSIS_PROCEDURES.md - Detailed implementation pseudocode for each phasetooluniverse-variant-interpretation - For SNVs and small indelsMake 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.
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tooluniverse-structural-variant-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
tooluniverse-structural-variant-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
Keeps context tight: tooluniverse-structural-variant-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for tooluniverse-structural-variant-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
Keeps context tight: tooluniverse-structural-variant-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend tooluniverse-structural-variant-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
tooluniverse-structural-variant-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
Registry listing for tooluniverse-structural-variant-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.
tooluniverse-structural-variant-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.
tooluniverse-structural-variant-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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