tooluniverse-structural-variant-analysis

mims-harvard/tooluniverse · updated Apr 8, 2026

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$npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-structural-variant-analysis
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summary

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.

skill.md

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.

KEY PRINCIPLES:

  1. Report-first approach - Create SV_analysis_report.md FIRST, then populate progressively
  2. ACMG-style classification - Pathogenic/Likely Pathogenic/VUS/Likely Benign/Benign with explicit evidence
  3. Evidence grading - Grade all findings by confidence level (High/Moderate/Limited)
  4. Dosage sensitivity critical - Gene dosage effects drive SV pathogenicity
  5. Breakpoint precision matters - Exact gene disruption vs dosage-only effects
  6. Population context essential - gnomAD SVs for frequency assessment
  7. English-first queries - Always use English terms in tool calls. Respond in the user's language

Triggers

Use this skill when users:

  • Ask about structural variant interpretation
  • Have CNV data from array or sequencing
  • Ask "is this deletion/duplication pathogenic?"
  • Need ACMG classification for SVs
  • Want to assess gene dosage effects
  • Ask about chromosomal rearrangements
  • Have large-scale genomic alterations requiring interpretation

SV Pathogenicity Reasoning (Start Here)

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?

  • Deletion: loss of one copy. Pathogenic if any contained gene is haploinsufficient (ClinGen HI score 3, pLI >= 0.9). A deletion of a dosage-insensitive gene in a gene-dense region may be benign even if large.
  • Duplication: gain of one copy. Pathogenic if any contained gene is dosage-sensitive (ClinGen TS score 3). Duplications can also disrupt gene regulation if tandem (disrupts reading frame at junction) or if they separate a gene from its enhancer.
  • Inversion: no copy number change. Pathogenic only at the breakpoints: if one breakpoint falls within an exon (truncation) or separates a gene from its regulatory element. Inversions entirely within gene-poor, regulatory-poor regions are often benign.
  • Translocation: pathogenic if a breakpoint disrupts a coding region or creates a pathogenic fusion gene. Balanced translocations in parents of affected children warrant special scrutiny.
  • Complex rearrangements: assess each segment and each breakpoint independently.

2. Is the disrupted gene dosage-sensitive?

  • ClinGen HI score 3 = definitive haploinsufficiency (deletion of this gene is pathogenic)
  • ClinGen HI score 2 = likely haploinsufficient
  • pLI >= 0.9 = strong LoF intolerance (supporting haploinsufficiency)
  • ClinGen TS score 3 = definitive triplosensitivity (duplication is pathogenic)
  • If no ClinGen data: use OMIM inheritance (autosomal dominant = often dosage-sensitive) as weaker evidence

3. Does the population frequency contextualize the SV?

  • =1% frequency in gnomAD SV = BA1 (likely benign unless phenotype is extreme)

  • <0.01% = supports pathogenicity (PM2)
  • Present in unaffected parents = weak evidence against pathogenicity, but not conclusive

4. Is there clinical precedent?

  • Identical SV in ClinVar as Pathogenic/Likely Pathogenic = strong evidence (PS1)
  • De novo occurrence = strong evidence for pathogenicity (PS2)
  • Phenotype match to known gene-disease association = supporting evidence (PP4)

Document this reasoning before computing the final score.


Workflow Overview

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

Phase 1: SV Identity & Classification

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.


Phase 2: Gene Content Analysis

Goal: Annotate all genes affected by the SV.

Tools:

  • ensembl_lookup_gene - gene structure, coordinates, exons
  • NCBIGene_search - official symbol, aliases, description
  • Gene_Ontology_get_term_info - biological process, molecular function
  • OMIM_search, OMIM_get_entry - disease associations, inheritance
  • DisGeNET_search_gene - gene-disease association scores

Classify 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.


Phase 3: Dosage Sensitivity Assessment

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 level
  • gnomad_search_variants - pLI scores for LoF intolerance
  • OMIM_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.


Phase 4: Population Frequency Context

Goal: Determine if SV is common (likely benign) or rare (supports pathogenicity).

Tools:

  • gnomad_search_variants - population SV frequencies
  • ClinVar_search_variants - known pathogenic/benign SVs
  • ClinGen_search_dosage_sensitivity - patient SVs with phenotypes

Use >=70% reciprocal overlap to define "same" SV for comparison. A frequency >=1% triggers BA1 unless there is very strong clinical evidence to override.


Phase 5: Pathogenicity Scoring

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.


Phase 6: Literature & Clinical Evidence

Goal: Find case reports, functional studies, and clinical validation.

Tools:

  • PubMed_search_articles - peer-reviewed literature
  • EuropePMC_search_articles - additional coverage
  • ClinGen_search_dosage_sensitivity - patient case database

Search strategies: gene-specific dosage sensitivity papers, SV-specific case reports, phenotype-gene associations. See ANALYSIS_PROCEDURES.md Phase 6.


Phase 7: ACMG-Adapted Classification

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:

  • PVS1 applies to deletions of genes with ClinGen HI score >= 2 or pLI >= 0.9
  • PS2 requires confirmed de novo status (check parental genotypes if available)
  • PM2 requires absence from population databases at >=70% reciprocal overlap

For complete evidence code tables and classification algorithm, see CLASSIFICATION_GUIDE.md.


Output

Create report using the template in REPORT_TEMPLATE.md. Name files as:

SV_analysis_[TYPE]_chr[CHR]_[START]_[END]_[GENES].md

Required Tools Reference

  • 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)

When NOT to Use This Skill

  • Single nucleotide variants (SNVs) - Use tooluniverse-variant-interpretation
  • Small indels (<50 bp) - Use variant interpretation skill
  • Somatic variants in cancer - Different framework needed
  • Mitochondrial variants - Specialized interpretation required
  • Repeat expansions - Different mechanism

Use this skill for structural variants >=50 bp requiring dosage sensitivity assessment and ACMG-adapted classification.


Reference Files

  • EXAMPLES.md - Sample SV interpretations with worked examples
  • CLASSIFICATION_GUIDE.md - ACMG criteria, scoring system, evidence codes, special scenarios, clinical recommendations
  • REPORT_TEMPLATE.md - Full report template with section structure and file naming
  • ANALYSIS_PROCEDURES.md - Detailed implementation pseudocode for each phase

External References

how to use tooluniverse-structural-variant-analysis

How to use tooluniverse-structural-variant-analysis on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add tooluniverse-structural-variant-analysis
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/mims-harvard/tooluniverse --skill tooluniverse-structural-variant-analysis

The skills CLI fetches tooluniverse-structural-variant-analysis from GitHub repository mims-harvard/tooluniverse and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/tooluniverse-structural-variant-analysis

Reload or restart Cursor to activate tooluniverse-structural-variant-analysis. Access the skill through slash commands (e.g., /tooluniverse-structural-variant-analysis) or your agent's skill management interface.

Security & Verification Notice

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.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

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

Competitive Analysis

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

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

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

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ 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.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.841 reviews
  • Sophia Huang· Dec 24, 2024

    tooluniverse-structural-variant-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ren Bansal· Dec 20, 2024

    tooluniverse-structural-variant-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Ganesh Mohane· Dec 12, 2024

    Keeps context tight: tooluniverse-structural-variant-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Layla Ndlovu· Dec 8, 2024

    Registry listing for tooluniverse-structural-variant-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Tariq Jain· Nov 27, 2024

    Keeps context tight: tooluniverse-structural-variant-analysis is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Sophia Sethi· Nov 23, 2024

    I recommend tooluniverse-structural-variant-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • William Anderson· Nov 15, 2024

    tooluniverse-structural-variant-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Rahul Santra· Nov 3, 2024

    Registry listing for tooluniverse-structural-variant-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • Pratham Ware· Oct 22, 2024

    tooluniverse-structural-variant-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • Hiroshi Desai· Oct 18, 2024

    tooluniverse-structural-variant-analysis is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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