Agent skill

Bio Long Read Sequencing Structural Variants

by GPTomics in GPTomics/bioSkills

Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based…

MITAuto-check passedResearch & Science

Install Bio Long Read Sequencing Structural Variants

skills CLI
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-structural-variants --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/long-read-sequencing/structural-variants .claude/skills/bio-long-read-sequencing-structural-variants && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
bio-long-read-sequencing-structural-variants
GitHub stars
1.2k
Used in
1 other repo
Token cost
~3.4k tokens
SKILL.md length
1,401 words
Files
3
Skills in repo
559
Repo updated
First seen
Licence
MIT

At a glance

Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based…

  • Works in 3 steps: The tandem-repeat BED, the aligner, and… → Without a TR BED, one event fragments… → truvari refine exists precisely to…
  • Calling germline
  • SKILL.md covers Version Compatibility, The Single Most Important…, Caller Taxonomy and Decision Tree by Scenario, plus 9 more sections
  • Runs Shell scripts from its folder

What it does

Bio Long Read Sequencing Structural Variants is an agent skill from GPTomics/bioSkills. Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based callers, joint-genotypes cohorts via the Sniffles2 .snf workflow, and benchmarks with Truvari against GIAB. Covers why an SV call is a representation artifact (the tandem-repeat BED, aligner, and Truvari params set precision/recall as much as the caller), the cuteSV per-platform parameter trap, soft-clipped supplementary…

Its SKILL.md is about 3.4k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/sv_calling.sh` and `usage-guide.md`).

It sits in Research & Science, covering Bioinformatics. The repository describes itself as: a set of SKILLS.md for doing bioinformatics with agents like claude code. The licence is MIT.

When your agent uses it

  • Calling germline
  • Somatic SVs from ONT/HiFi reads
  • Joint-genotyping a cohort
  • Tuning an SV caller

Example prompts

  • “Use the bio-long-read-sequencing-structural-variants skill to detect structural variants (deletions, insertions, inversions, duplications…”
  • “/bio-long-read-sequencing-structural-variants”

Requirements

  • A Bash shell

Workflow steps

3 steps, taken from the first numbered list in SKILL.md.

  1. The tandem-repeat BED, the aligner, and the Truvari parameters decide precision/recall as much as the caller does. A claim like "caller X…
  2. Without a TR BED, one event fragments into several false-positive calls with inconsistent breakpoints. --tandem-repeats makes clustering…
  3. truvari refine exists precisely to re-harmonize representations within TR regions; benchmarking TR-dense regions without it systematically…

What it can do on your machine

Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships script files (Shell), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Bio Long Read Sequencing Structural Variants loads about 3.4k tokens when it runs. Until then it costs about 199 tokens; SKILL.md has 1,401 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~199
When it runs · the whole SKILL.md, loaded when a task matches
~3.4k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,401 words, ~3,351 tokens.

Download SKILL.mdSave it as .claude/skills/bio-long-read-sequencing-structural-variants/SKILL.md (or your agent's skills folder). This skill also uses 2 other files; get the full folder from GitHub.
name
bio-long-read-sequencing-structural-variants
description
Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based callers, joint-genotypes cohorts via the Sniffles2 .snf workflow, and benchmarks with Truvari against GIAB. Covers why an SV call is a representation artifact (the tandem-repeat BED, aligner, and Truvari params set precision/recall as much as the caller), the cuteSV per-platform parameter trap, soft-clipped supplementary alignments as the SV substrate, and the somatic/mosaic boundary to Severus/nanomonsv. Use when calling germline or somatic SVs from ONT/HiFi reads, joint-genotyping a cohort, choosing or tuning an SV caller, or benchmarking SV calls.
tool_type
cli
primary_tool
sniffles

Version Compatibility

Reference examples tested with: Sniffles 2.2+, cuteSV 2.1+, minimap2 2.28+, samtools 1.19+, truvari 4.0+.

Before using code patterns, verify installed versions match. If versions differ:

  • CLI: <tool> --version then <tool> --help to confirm flags

Results depend on inputs that outlive the binary version - record them:

  • The reference-matched tandem-repeat BED supplied to the caller (Sniffles --tandem-repeats) drives the FP rate in repeats more than any other setting. Record which TR BED was used.
  • Benchmark numbers depend on the region set + TR handling + Truvari params; record all three.
  • cuteSV parameters are platform-specific (ONT vs HiFi vs CLR); the defaults are not platform-appropriate.

If code throws an error, introspect the installed tool (sniffles --help, cuteSV --help) and adapt the example to the actual API rather than retrying.

Long-Read Structural Variants

"Find structural variants in my long reads" -> Map with the SV-ready preset (soft-clipped supplementaries), call with a TR-aware caller, and benchmark stating the region set and Truvari params.

  • CLI: sniffles --input aln.bam --vcf svs.vcf --reference ref.fa --tandem-repeats TR.bed

Long reads are the killer app for SVs: a single read spans the breakpoint (within-read CIGAR or split alignment) and resolves repeats short reads cannot. By convention SV = >=50 bp; the 30-100 bp range is a VNTR-dominated gray zone where callers disagree most.

The Single Most Important Modern Insight -- An SV Call Is a Representation Artifact as Much as a Biological Fact

In tandem repeats and segmental duplications, the same biological event has many valid VCF encodings - a deletion can be written as the reciprocal insertion on the other allele, and a VNTR expansion's breakpoints slide freely across repeat units. Consequently:

  1. The tandem-repeat BED, the aligner, and the Truvari parameters decide precision/recall as much as the caller does. A claim like "caller X has F1 0.95" is meaningless without also stating the region set, the TR BED supplied to the caller, and the Truvari params - change any one and the number moves more than the gap between callers.
  2. Without a TR BED, one event fragments into several false-positive calls with inconsistent breakpoints. --tandem-repeats makes clustering repeat-aware (widening the merge window inside annotated TRs) - the single biggest FP-reduction lever, not a nicety.
  3. truvari refine exists precisely to re-harmonize representations within TR regions; benchmarking TR-dense regions without it systematically understates recall.

Caller Taxonomy

ToolRegimeBest forCitation
Sniffles2germline + population + mosaicthe default germline workhorse; cohort joint genotyping; .snf mergeSmolka 2024 Nat Biotechnol 42:1571
cuteSVgermlinehigh sensitivity, speed; per-platform tuning requiredJiang 2020 Genome Biol 21:189
SVIMgermlinescores (not hard-filters) SVs; good INS detectionHeller 2019 Bioinformatics 35:2907
pbsvgermline (PacBio)two-step discover->call; official PacBio toolPacBio (no journal paper)
NanoVargermline, low-depth4-8x ONT clinicalTham 2020 Genome Biol 21:56
dipcall / SVIM-asm / PAVassembly-based germlinemost accurate single sample with phased HiFi; truth-set generationLi 2018; Heller 2021; Ebert 2021
Severussomatic (tumor-normal)cancer T/N, complex/subclonalKeskus 2026 Nat Biotechnol
nanomonsvsomatic (tumor-normal)precise somatic breakpoints, MEIShiraishi 2023 NAR 51:e74
SVision-prode novo + somatic, complexresolving nested CSVsWang 2025 Nat Biotechnol 43:181

Decision Tree by Scenario

ScenarioRecommendedWhy
Single ONT/HiFi germline sampleSniffles2 + --tandem-repeatsTR-aware, auto support, fast
Cohort germlineSniffles2 per-sample .snf -> mergere-genotypes from raw signal; true joint genotypes
Maximum sensitivity / speedcuteSV with the platform-matched param setper-platform tuning is mandatory
Phased HiFi, want best per-sample accuracyassembly-based (dipcall/SVIM-asm) -> hifi-assemblyresolves the alt haplotype directly
Tumor-normal somatic SVsSeverus or nanomonsvpaired callers; Sniffles --mosaic is single-sample only
Low-VAF mosaic in one sampleSniffles2 --mosaiclowers support, reports VAF (not a T/N caller)
Low coverage (4-8x)NanoVardesigned for low-depth clinical
BenchmarkingTruvari (+refine) vs GIAB Tier1/CMRGthe field standard; state region + params

Alignment for SV Calling

Map with minimap2 (the modern default; NGMLR is a higher-precision/slower legacy niche for Sniffles). Use the platform preset and keep soft-clipped supplementary alignments - split-read callers reconstruct breakpoints from the clipped sequence on those records.

bash
minimap2 -ax map-ont --MD -Y ref.fa ont.fq.gz | samtools sort -o aln.bam && samtools index aln.bam
#   -Y keeps SEQ on supplementaries (the SV substrate); --MD for cuteSV; map-hifi/map-pb for PacBio

Sniffles2 - germline and the .snf population workflow

bash
# Single sample (always supply --reference for INS sequence and --tandem-repeats for repeats)
sniffles --input aln.bam --vcf svs.vcf --reference ref.fa --tandem-repeats human_GRCh38_TR.bed

# Cohort: per-sample .snf signature index, then merge + joint-genotype
sniffles --input s1.bam --snf s1.snf --reference ref.fa --tandem-repeats TR.bed
sniffles --input s2.bam --snf s2.snf --reference ref.fa --tandem-repeats TR.bed
sniffles --input s1.snf s2.snf --vcf cohort.vcf --reference ref.fa

# Force-call / regenotype a known SV set in a new sample
sniffles --input new.bam --genotype-vcf known_svs.vcf --vcf genotyped.vcf

# Single-sample low-VAF / mosaic (NOT a tumor-normal caller)
sniffles --input tumor.bam --vcf mosaic.vcf --mosaic

The .snf is a binary signature index (NOT a VCF - never bcftools it); it retains sub-threshold signatures so the merge re-genotypes an SV even in a sample that did not independently pass support.

cuteSV - the per-platform parameter trap

cuteSV's defaults are not platform-appropriate; the README gives distinct sets by error rate. --genotype is OFF by default. Positional args: cuteSV <bam> <ref> <out.vcf> <work_dir>. Force-calling moved to the separate cuteFC tool.

Platform--max_cluster_bias_INS--diff_ratio_merging_INS--max_cluster_bias_DEL--diff_ratio_merging_DEL
ONT1000.31000.3
PacBio HiFi/CCS10000.910000.5
PacBio CLR1000.32000.5
bash
mkdir cutesv_work
cuteSV aln.bam ref.fa cutesv.vcf cutesv_work --genotype \
  --max_cluster_bias_INS 100 --diff_ratio_merging_INS 0.3 \
  --max_cluster_bias_DEL 100 --diff_ratio_merging_DEL 0.3   # ONT set

Benchmarking with Truvari

bash
truvari bench --base giab_tier1.vcf.gz --comp calls.vcf.gz \
  --includebed tier1_regions.bed --pctseq 0.7 --refdist 500 --passonly -o bench/
truvari refine bench/        # re-harmonize TR-region representations for a fair comparison

--pctseq (default 0.7) compares the actual inserted/deleted sequence, not just coordinates - set 0 for depth-based callers lacking alt sequence, keep 0.7 for long-read callers. Region set dominates the headline: Tier1 (resolvable INS/DEL >=50 bp) overstates whole-genome performance; CMRG reflects hard clinical loci. Tier1 v0.6 is INS/DEL only - do not report INV recall against it.

Per-Method Failure Modes

One VNTR fragments into many false positives

Trigger: calling in tandem repeats without a TR BED. Mechanism: the breakpoint slides across repeat units, scattering signatures. Symptom: several calls with inconsistent breakpoints where one event exists. Fix: supply --tandem-repeats to the caller; truvari refine when benchmarking.

Show full SKILL.md (547 more words)Show less
cuteSV defaults inflate or fragment calls

Trigger: running cuteSV with one parameter set across platforms. Mechanism: HiFi settings over-merge ONT noise; ONT settings fragment clean HiFi signatures. Symptom: FP inflation or split calls. Fix: use the platform-matched set; remember --genotype is off by default.

Missing insertion sequence / breakpoints

Trigger: Sniffles without --reference, or alignment without -Y. Mechanism: no reference -> no ALT sequence; hard-clipped supplementaries -> lost breakpoint sequence. Symptom: INS lack sequence; imprecise breakpoints. Fix: add --reference and align with -Y.

Treating Sniffles --mosaic as a cancer caller

Trigger: somatic SV calling with single-sample --mosaic. Mechanism: mosaic mode lowers support in one sample; it has no normal to subtract. Symptom: germline SVs reported as somatic; FP at low VAF. Fix: Severus or nanomonsv (paired tumor-normal).

Comparing F1 across studies that handled repeats differently

Trigger: quoting F1 without region + TR BED + Truvari params. Mechanism: representation handling moves the number more than the caller. Symptom: apples-to-oranges comparisons. Fix: fix the region set, TR BED, and Truvari params; run truvari refine.

Quantitative Thresholds

ThresholdSourceRationale
SV >= 50 bpGIAB convention30-100 bp is a VNTR gray zone where callers disagree
Sniffles --minsvlen 35, --mapq 25, --minsupport autoSniffles2 manpagethe actual defaults (support is coverage-derived, not a fixed 3)
Coverage ~20-30x germline; >30-60x mosaic/somaticSV practicelarge SVs callable from 5-10x; low-VAF needs depth
Truvari --pctseq 0.7, --refdist 500English 2022sequence-aware INS matching; loosen refdist to 1000 only for fuzzy callers
cuteSV params per platformcuteSV READMEerror rate sets cluster bias / merge ratio

Common Errors

Error / symptomCauseSolution
Many FP calls in repeatsno TR BEDsupply --tandem-repeats
cuteSV VCF has no GT--genotype off by defaultadd --genotype
Cannot bcftools the .snf.snf is a binary signature indexuse it as Sniffles input, not a VCF
INS records lack sequence--reference not suppliedadd --reference ref.fa
Imprecise/missing breakpointssupplementaries hard-clippedalign with minimap2 -Y
Looking for cuteSV force-calling flagmoved to cuteFCuse the cuteFC tool
Somatic SVs from a single samplegermline/mosaic callerSeverus / nanomonsv (paired)

References

  • Smolka M, Paulin LF, Grochowski CM, et al. 2024. Detection of mosaic and population-level structural variants with Sniffles2. Nat Biotechnol 42:1571-1580.
  • Jiang T, Liu Y, Jiang Y, et al. 2020. Long-read-based human genomic structural variation detection with cuteSV. Genome Biol 21:189.
  • Heller D, Vingron M. 2019. SVIM: structural variant identification using mapped long reads. Bioinformatics 35:2907-2915.
  • English AC, Menon VK, Gibbs RA, Metcalf GA, Sedlazeck FJ. 2022. Truvari: refined structural variant comparison preserves allelic diversity. Genome Biol 23:271.
  • Zook JM, Hansen NF, Olson ND, et al. 2020. A robust benchmark for detection of germline large deletions and insertions. Nat Biotechnol 38:1347-1355.
  • Wagner J, Olson ND, Harris L, et al. 2022. Curated variation benchmarks for challenging medically relevant autosomal genes (CMRG). Nat Biotechnol 40:672-680.
  • Keskus AG, Bryant A, Ahmad T, et al. 2026. Severus detects somatic structural variation and complex rearrangements in cancer genomes using long-read sequencing. Nat Biotechnol 44:247-257.
  • long-read-alignment - SV-ready mapping (-Y soft-clip, platform preset)
  • basecalling - Read accuracy/length that gates breakpoint precision
  • clair3-variants - Small variants (<50 bp) are Clair3's job, not an SV caller's
  • haplotype-phasing - Haplotag the BAM for haplotype-specific / phased SVs
  • genome-assembly/hifi-assembly - Phased assembly for assembly-based SV calling
  • variant-calling/structural-variant-calling - The variant-calling-side SV view
  • variant-calling/vcf-manipulation - Filter/merge the SV VCFs
  • genome-intervals/gtf-gff-handling - Annotate SVs against gene models

© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 2 other files in long-read-sequencing/structural-variants of GPTomics/bioSkills.

  • SKILL.md
  • examples/sv_calling.sh
  • usage-guide.md

Open the folder on GitHubat commit d91ed3d

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in GPTomics/bioSkills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Bio Long Read Sequencing Structural Variants next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Bio Long Read Sequencing Structural Variants compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Bio Long Read Sequencing Structural Variants this skillGPTomics/bioSkills1.2k1 repos~3.4kAutomated safety check: PassMIT
Alphagenome Single Variant Analysisgoogle-deepmind/science-skills3.2k2 repos~3kAutomated safety check: NotesApache-2.0
13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills48k1 repos~3.2kAutomated safety check: PassMIT
Clinvar Databasegoogle-deepmind/science-skills3.2k2 repos~3.9kAutomated safety check: NotesApache-2.0
Metabolic Study Planneraiming-lab/AutoResearchClaw15k—~1.9kAutomated safety check: PassMIT
Dbsnp Databasegoogle-deepmind/science-skills3.2k2 repos~3.4kAutomated safety check: NotesApache-2.0

Similar skills

  • Alphagenome Single Variant Analysis

    google-deepmind/science-skills

    Analyzes genetic variant effects on gene expression (RNA-seq), chromatin accessibility (DNASE), histone marks (ChIP), and transcription factors using the AlphaGenome API.

    3.2k GitHub starsUsed in 2 repos~3k tokens
    Research & ScienceAuto-check: notes
  • 13C Metabolic Flux Analysis

    K-Dense-AI/scientific-agent-skills

    Estimates reaction fluxes inside cells from steady-state carbon-13 labeling data with a bundled mfapy-based solver, and reports which fluxes the data pin down.

    48k GitHub starsUsed in 1 repo~3.2k tokens
    Research & ScienceAuto-check passed
  • Clinvar Database

    google-deepmind/science-skills

    A skill your agent uses when needing clinical significance, pathogenicity classifications (e.g., Pathogenic, Benign, VUS), clinical evidence rationales, or finding "hard positive" benchmark controls…

    3.2k GitHub starsUsed in 2 repos~3.9k tokens
    Research & ScienceAuto-check: notes
  • Metabolic Study Planner

    aiming-lab/AutoResearchClaw

    Turns a broad metabolic modelling topic into a concrete, paper-shaped plan with organism, model, perturbations, metrics and figures before any FBA code is written.

    15k GitHub stars~1.9k tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed
  • Dbsnp Database

    google-deepmind/science-skills

    A skill your agent uses when you want to look up, map, and search for short genetic variants (SNPs, indels) in NCBI's dbSNP database.

    3.2k GitHub starsUsed in 2 repos~3.4k tokens
    Research & ScienceAuto-check: notes
  • MFA Pipeline Orchestrator

    aiming-lab/AutoResearchClaw

    Runs a metabolic flux analysis from model loading to phenotype prediction and figures by handing work to four sub-agents in sequence.

    15k GitHub stars~923 tokensUpdated 1 mo ago
    Research & ScienceAuto-check passed

More from GPTomics/bioSkills

All 559 skills in this repo
  • Bio Alignment Io

    GPTomics/bioSkills

    Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.

    1.2k GitHub starsUsed in 3 repos~4.9k tokens
    Auto-check passed
  • bioSkills Installer

    GPTomics/bioSkills

    Installs the bioSkills collection of 425 bioinformatics skills in one step, or only chosen categories, so sequencing, RNA-seq, single-cell and variant tasks get specialized help.

    1.2k GitHub starsUsed in 1 repo~789 tokens
    Auto-check passed
  • Bio Write Sequences

    GPTomics/bioSkills

    Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.

    1.2k GitHub starsUsed in 3 repos~2.1k tokens
    Auto-check passed
  • Amplicon Primer Clipping

    GPTomics/bioSkills

    Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.

    1.2k GitHub starsUsed in 2 repos~2.2k tokens
    Auto-check passed
  • Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.

    1.2k GitHub starsUsed in 2 repos~3.6k tokens
    Auto-check passed
  • Bio Alignment Indexing

    GPTomics/bioSkills

    Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.

    1.2k GitHub starsUsed in 2 repos~2.4k tokens
    Auto-check passed

Questions about Bio Long Read Sequencing Structural Variants

What does Bio Long Read Sequencing Structural Variants do?

Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based…. Bio Long Read Sequencing Structural Variants is an agent skill from GPTomics/bioSkills.snf workflow, and benchmarks with Truvari against GIAB.

When should I use Bio Long Read Sequencing Structural Variants?

Bio Long Read Sequencing Structural Variants fits situations like: calling germline; somatic SVs from ONT/HiFi reads; joint-genotyping a cohort; tuning an SV caller.

How do I install Bio Long Read Sequencing Structural Variants in Claude Code?

Run `npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a claude-code`. Or copy the skill folder (long-read-sequencing/structural-variants in GPTomics/bioSkills) into .claude/skills/bio-long-read-sequencing-structural-variants in your project. Claude Code loads it when a task matches its description.

How do I install Bio Long Read Sequencing Structural Variants in Codex?

Run `npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a codex`. Or copy the skill folder (long-read-sequencing/structural-variants in GPTomics/bioSkills) into .agents/skills/bio-long-read-sequencing-structural-variants in your project. Codex loads it when a task matches its description.

Can I use Bio Long Read Sequencing Structural Variants in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/bio-long-read-sequencing-structural-variants, .gemini/skills/bio-long-read-sequencing-structural-variants, .github/skills/bio-long-read-sequencing-structural-variants and .opencode/skills/bio-long-read-sequencing-structural-variants in your project.

What does Bio Long Read Sequencing Structural Variants need to run?

Going by SKILL.md and its folder, Bio Long Read Sequencing Structural Variants needs a shell for the scripts in its folder. Our summary lists: A Bash shell.

Does Bio Long Read Sequencing Structural Variants access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Bio Long Read Sequencing Structural Variants safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Bio Long Read Sequencing Structural Variants use?

Bio Long Read Sequencing Structural Variants is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Bio Long Read Sequencing Structural Variants use?

About 3.4k tokens (SKILL.md is roughly 13k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Bio Long Read Sequencing Structural Variants?

Skills that share tags, products or a category with Bio Long Read Sequencing Structural Variants: Alphagenome Single Variant Analysis (google-deepmind/science-skills, 3.2k stars), 13C Metabolic Flux Analysis (K-Dense-AI/scientific-agent-skills, 48k stars), Clinvar Database (google-deepmind/science-skills, 3.2k stars) and Metabolic Study Planner (aiming-lab/AutoResearchClaw, 15k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Bio Long Read Sequencing Structural Variants?

GPTomics (a GitHub organization) maintains it in GPTomics/bioSkills, which has 1,218 GitHub stars. The repository holds 559 skills in this directory. The repository was last updated on August 15, 2026.

Source: GPTomics/bioSkills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.