structural-variant-detector
Structural variant detection skill for identifying CNVs, inversions, translocations, and complex rearrangements
Best use case
structural-variant-detector is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Structural variant detection skill for identifying CNVs, inversions, translocations, and complex rearrangements
Teams using structural-variant-detector should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/structural-variant-detector/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How structural-variant-detector Compares
| Feature / Agent | structural-variant-detector | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Structural variant detection skill for identifying CNVs, inversions, translocations, and complex rearrangements
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Structural Variant Detector Skill ## Purpose Enable structural variant detection for identifying CNVs, inversions, translocations, and complex rearrangements. ## Capabilities - Split-read and paired-end SV calling - Copy number variation detection - Mobile element insertion detection - Complex SV resolution - SV annotation and visualization - Multi-caller integration ## Usage Guidelines - Use multiple callers for comprehensive detection - Integrate results from different algorithms - Validate SVs with independent methods - Annotate SVs with functional impact - Visualize SVs for manual review - Document caller combinations and filters ## Dependencies - Manta - DELLY - CNVkit - LUMPY - GRIDSS ## Process Integration - Whole Genome Sequencing Pipeline (wgs-analysis-pipeline) - Tumor Molecular Profiling (tumor-molecular-profiling) - Long-Read Sequencing Analysis (long-read-analysis)
Related Skills
homoglyph-detector
Byte-level Unicode homoglyph detection for identifying invisible character substitutions in code
geant4-detector-simulator
Geant4 detector simulation skill for particle transport, detector geometry, and physics process modeling
loop-invariant-generator
Automatically generate and verify loop invariants for algorithm correctness proofs
fea-structural-engine
Finite Element Analysis skill for linear and nonlinear structural analysis, modal analysis, and load combination processing
vep-variant-annotator
Variant Effect Predictor skill for comprehensive variant annotation with clinical database integration
gatk-variant-caller
GATK best practices skill for germline and somatic variant calling with joint genotyping
fusion-gene-detector
Gene fusion detection skill for oncology applications with multiple caller integration
deepvariant-caller
DeepVariant deep learning variant calling skill for high-accuracy SNV and indel detection
acmg-variant-classifier
ACMG/AMP variant classification skill for systematic pathogenicity assessment
fea-structural
Expert FEA skill for aerospace structural analysis workflows
memory-leak-detector
Detect memory leaks in desktop applications through heap analysis and object tracking
fairlearn-bias-detector
Fairness assessment skill using Fairlearn for bias detection, mitigation, and compliance reporting.