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genomics-sv-detection

Analyze and classify structural variants from VCF data for bioinformatics workflows.

Install

mkdir -p .claude/skills/genomics-sv-detection && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/11238" && unzip -o skill.zip -d .claude/skills/genomics-sv-detection && rm skill.zip

Installs to .claude/skills/genomics-sv-detection

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Load when summarising structural variants from an SV VCF (DEL / DUP / INV / TRA) — BND-notation parsing, size classification, per-type counts. Skip when working with small SNVs / indels (use `genomics-variant-calling`) or calling SVs from BAM (run Manta / Delly / Sniffles first).
280 chars✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Intermediate

Key capabilities

  • Parses SV-flavored VCF files
  • Classifies structural variants by size
  • Generates per-type counts for DEL/DUP/INV/TRA
  • Resolves BND notation
  • Produces summary CSV and Markdown reports

How it works

The tool reads INFO/SVTYPE fields from a VCF, calculates absolute SVLEN for size binning, and aggregates counts into a structured report.

Inputs & outputs

You give it
SV-flavored VCF file
You get back
CSV summary table and Markdown report

When to use genomics-sv-detection

  • Generate summary table of structural variants
  • Classify SVs by size and type
  • Parse BND notation from VCF outputs

About this skill

genomics-sv-detection

When to use

The user has an SV VCF (from Manta, Delly, Lumpy, Sniffles, etc.) and wants per-type counts (DEL / DUP / INV / TRA / INS), size classification (small 50 bp–1 kb / medium 1 kb–100 kb / large 100 kb–10 Mb / very-large > 10 Mb), and BND breakend resolution.

The script does NOT call SVs from a BAM. Run an external SV caller first; this skill summarises its VCF output.

Inputs & Outputs

<!-- AUTO-GENERATED from skill.yaml (interface) — do not edit by hand. Regenerate: python scripts/generate_skill_md.py <skill_dir> -->

Inputs

  • File types: .vcf

Outputs

  • tables/structural_variants.csv
  • report.md
  • result.json

Flow

  1. Load VCF (--input <sv.vcf>) or generate a demo SV VCF at output_dir/demo_structural_variants.vcf with --n-svs records (sv_detection.py:170).
  2. Parse records; read INFO/SVTYPE (sv_detection.py:103). Records without INFO/SVTYPE (e.g. pure BND ALT notation from Manta) classify as UNKNOWN — there is NO BND-to-TRA resolution.
  3. Compute abs(SVLEN) for size classification (sv_detection.py:105); bin into size classes; aggregate per-type counts.
  4. Write tables/structural_variants.csv (sv_detection.py:343) + report.md + result.json (:346).

Gotchas

  • No SV caller is invoked. This skill ingests an SV VCF — it does NOT run Manta / Delly / Lumpy / Sniffles. To CALL SVs, run an external pipeline first.
  • --input REQUIRED unless --demo. sv_detection.py:330 raises ValueError("--input required when not using --demo"); non-existent paths raise FileNotFoundError at :333.
  • --n-svs only affects --demo (sv_detection.py:319, default 100). Silently ignored when --input is set.
  • Pure BND records without INFO/SVTYPE classify as UNKNOWN. sv_detection.py:103 reads only INFO/SVTYPE; there is no BND ALT-notation parser and no MATEID pairing logic. Manta callsets that emit translocations as paired BND records (without an SVTYPE=TRA INFO field) will appear as UNKNOWN, not TRA. Pre-process with bcftools view -i 'INFO/SVTYPE!=""' or with a Manta-specific BND→TRA resolver upstream.
  • SVLEN is stored as absolute value in the CSV. sv_detection.py:105 writes abs(int(info.get("SVLEN", end - pos))) — a 1234-bp deletion becomes 1234 in the CSV regardless of the input sign. The original signed SVLEN is NOT preserved.
  • Demo VCF mixes DEL / DUP / INV / TRA at fixed proportions. Useful for orchestrator smoke tests; not biologically meaningful.

Key CLI

# Demo (100 synthetic SVs)
python omicsclaw.py run genomics-sv-detection --demo --output /tmp/sv_demo

# Custom demo size
python omicsclaw.py run genomics-sv-detection --demo --n-svs 500 \
  --output /tmp/sv_demo_large

# Real SV VCF
python omicsclaw.py run genomics-sv-detection \
  --input manta_diploid.vcf --output results/

See also

  • references/parameters.md — every CLI flag
  • references/methodology.md — SVTYPE / BND semantics, size-class boundaries
  • references/output_contract.mdtables/structural_variants.csv schema
  • Adjacent skills: genomics-alignment (upstream — provides BAMs for SV callers), genomics-variant-calling (parallel — small SNVs / indels), genomics-cnv-calling (parallel — copy-number from depth, complementary to SV callers), genomics-variant-annotation (downstream — functional impact of breakpoints)

When not to use it

  • Calling SVs from BAM files
  • Processing small SNVs or indels
  • Resolving complex BND-to-TRA pairings without upstream processing

Prerequisites

pandasnumpy

Limitations

  • Does not call SVs from BAMs
  • Pure BND records without SVTYPE classify as UNKNOWN
  • SVLEN is stored as absolute value

How it compares

It focuses specifically on summarizing existing SV callsets rather than performing the computationally intensive task of calling variants from raw alignments.

Compared to similar skills

genomics-sv-detection side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
genomics-sv-detection (this skill)03moReviewIntermediate
llm-evaluation62moNo flagsAdvanced
evaluating-llms-harness37moReviewAdvanced
qutip47moReviewAdvanced

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