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.
Detects structural variants (deletions, insertions, inversions, duplications, translocations) from Oxford Nanopore and PacBio long-read alignments with Sniffles2, cuteSV, SVIM, and assembly-based…
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-structural-variants --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ 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-srcUse ~/.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/
Install the "bio-long-read-sequencing-structural-variants" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/structural-variants into .claude/skills/bio-long-read-sequencing-structural-variants/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-structural-variants", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/structural-variantsType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-structural-variants --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/long-read-sequencing/structural-variants .agents/skills/bio-long-read-sequencing-structural-variants && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "bio-long-read-sequencing-structural-variants" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/structural-variants into .agents/skills/bio-long-read-sequencing-structural-variants/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-structural-variants", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-structural-variants --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/long-read-sequencing/structural-variants .cursor/skills/bio-long-read-sequencing-structural-variants && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "bio-long-read-sequencing-structural-variants" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/structural-variants into .cursor/skills/bio-long-read-sequencing-structural-variants/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-structural-variants", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/GPTomics/bioSkills.git --path long-read-sequencing/structural-variants--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-structural-variants --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/long-read-sequencing/structural-variants .gemini/skills/bio-long-read-sequencing-structural-variants && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "bio-long-read-sequencing-structural-variants" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/structural-variants into .gemini/skills/bio-long-read-sequencing-structural-variants/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-structural-variants", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-structural-variantsInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .github/skills && cp -r skills-src/long-read-sequencing/structural-variants .github/skills/bio-long-read-sequencing-structural-variants && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "bio-long-read-sequencing-structural-variants" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/structural-variants into .github/skills/bio-long-read-sequencing-structural-variants/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-structural-variants", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add GPTomics/bioSkills --skill bio-long-read-sequencing-structural-variants -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install GPTomics/bioSkills bio-long-read-sequencing-structural-variants --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/GPTomics/bioSkills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/long-read-sequencing/structural-variants .opencode/skills/bio-long-read-sequencing-structural-variants && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "bio-long-read-sequencing-structural-variants" agent skill from https://github.com/GPTomics/bioSkills/tree/main/long-read-sequencing/structural-variants into .opencode/skills/bio-long-read-sequencing-structural-variants/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-long-read-sequencing-structural-variants", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
bio-long-read-sequencing-structural-variantsDetects 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. 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.
3 steps, taken from the first numbered list in SKILL.md.
Read from SKILL.md and the folder at commit d91ed3d. It shows what the files ask for, not the result of running them.
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.
Ships script files (Shell), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
No URLs in SKILL.md.
From URLs in SKILL.md, links to its own repository left out.
Names no API keys, tokens, secrets or passwords.
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
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.
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.
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.
The full file from GPTomics/bioSkills at commit d91ed3d, republished under its MIT licence (© GPTomics). 1,401 words, ~3,351 tokens.
.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.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:
<tool> --version then <tool> --help to confirm flagsResults depend on inputs that outlive the binary version - record them:
--tandem-repeats) drives the FP rate in repeats more than any other setting. Record which TR BED was used.If code throws an error, introspect the installed tool (sniffles --help, cuteSV --help) and adapt the example to the actual API rather than retrying.
"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.
sniffles --input aln.bam --vcf svs.vcf --reference ref.fa --tandem-repeats TR.bedLong 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.
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:
--tandem-repeats makes clustering repeat-aware (widening the merge window inside annotated TRs) - the single biggest FP-reduction lever, not a nicety.truvari refine exists precisely to re-harmonize representations within TR regions; benchmarking TR-dense regions without it systematically understates recall.| Tool | Regime | Best for | Citation |
|---|---|---|---|
| Sniffles2 | germline + population + mosaic | the default germline workhorse; cohort joint genotyping; .snf merge | Smolka 2024 Nat Biotechnol 42:1571 |
| cuteSV | germline | high sensitivity, speed; per-platform tuning required | Jiang 2020 Genome Biol 21:189 |
| SVIM | germline | scores (not hard-filters) SVs; good INS detection | Heller 2019 Bioinformatics 35:2907 |
| pbsv | germline (PacBio) | two-step discover->call; official PacBio tool | PacBio (no journal paper) |
| NanoVar | germline, low-depth | 4-8x ONT clinical | Tham 2020 Genome Biol 21:56 |
| dipcall / SVIM-asm / PAV | assembly-based germline | most accurate single sample with phased HiFi; truth-set generation | Li 2018; Heller 2021; Ebert 2021 |
| Severus | somatic (tumor-normal) | cancer T/N, complex/subclonal | Keskus 2026 Nat Biotechnol |
| nanomonsv | somatic (tumor-normal) | precise somatic breakpoints, MEI | Shiraishi 2023 NAR 51:e74 |
| SVision-pro | de novo + somatic, complex | resolving nested CSVs | Wang 2025 Nat Biotechnol 43:181 |
| Scenario | Recommended | Why |
|---|---|---|
| Single ONT/HiFi germline sample | Sniffles2 + --tandem-repeats | TR-aware, auto support, fast |
| Cohort germline | Sniffles2 per-sample .snf -> merge | re-genotypes from raw signal; true joint genotypes |
| Maximum sensitivity / speed | cuteSV with the platform-matched param set | per-platform tuning is mandatory |
| Phased HiFi, want best per-sample accuracy | assembly-based (dipcall/SVIM-asm) -> hifi-assembly | resolves the alt haplotype directly |
| Tumor-normal somatic SVs | Severus or nanomonsv | paired callers; Sniffles --mosaic is single-sample only |
| Low-VAF mosaic in one sample | Sniffles2 --mosaic | lowers support, reports VAF (not a T/N caller) |
| Low coverage (4-8x) | NanoVar | designed for low-depth clinical |
| Benchmarking | Truvari (+refine) vs GIAB Tier1/CMRG | the field standard; state region + params |
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.
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# 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 --mosaicThe .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'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 |
|---|---|---|---|---|
| ONT | 100 | 0.3 | 100 | 0.3 |
| PacBio HiFi/CCS | 1000 | 0.9 | 1000 | 0.5 |
| PacBio CLR | 100 | 0.3 | 200 | 0.5 |
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 settruvari 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.
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.
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.
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.
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).
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.
| Threshold | Source | Rationale |
|---|---|---|
| SV >= 50 bp | GIAB convention | 30-100 bp is a VNTR gray zone where callers disagree |
Sniffles --minsvlen 35, --mapq 25, --minsupport auto | Sniffles2 manpage | the actual defaults (support is coverage-derived, not a fixed 3) |
| Coverage ~20-30x germline; >30-60x mosaic/somatic | SV practice | large SVs callable from 5-10x; low-VAF needs depth |
Truvari --pctseq 0.7, --refdist 500 | English 2022 | sequence-aware INS matching; loosen refdist to 1000 only for fuzzy callers |
| cuteSV params per platform | cuteSV README | error rate sets cluster bias / merge ratio |
| Error / symptom | Cause | Solution |
|---|---|---|
| Many FP calls in repeats | no TR BED | supply --tandem-repeats |
| cuteSV VCF has no GT | --genotype off by default | add --genotype |
Cannot bcftools the .snf | .snf is a binary signature index | use it as Sniffles input, not a VCF |
| INS records lack sequence | --reference not supplied | add --reference ref.fa |
| Imprecise/missing breakpoints | supplementaries hard-clipped | align with minimap2 -Y |
| Looking for cuteSV force-calling flag | moved to cuteFC | use the cuteFC tool |
| Somatic SVs from a single sample | germline/mosaic caller | Severus / nanomonsv (paired) |
-Y soft-clip, platform preset)© GPTomics, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
SKILL.md and 2 other files in long-read-sequencing/structural-variants of GPTomics/bioSkills.
Open the folder on GitHubat commit d91ed3d
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.
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.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Bio Long Read Sequencing Structural Variants this skillGPTomics/bioSkills | 1.2k | 1 repos | ~3.4k | Automated safety check: Pass | MIT | |
| Alphagenome Single Variant Analysisgoogle-deepmind/science-skills | 3.2k | 2 repos | ~3k | Automated safety check: Notes | Apache-2.0 | |
| 13C Metabolic Flux AnalysisK-Dense-AI/scientific-agent-skills | 48k | 1 repos | ~3.2k | Automated safety check: Pass | MIT | |
| Clinvar Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.9k | Automated safety check: Notes | Apache-2.0 | |
| Metabolic Study Planneraiming-lab/AutoResearchClaw | 15k | — | ~1.9k | Automated safety check: Pass | MIT | |
| Dbsnp Databasegoogle-deepmind/science-skills | 3.2k | 2 repos | ~3.4k | Automated safety check: Notes | Apache-2.0 |
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.
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.
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…
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.
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.
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.
GPTomics/bioSkills
Read, write, and convert multiple sequence alignment files using Biopython Bio.AlignIO.
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.
GPTomics/bioSkills
Write biological sequences to files (FASTA, FASTQ, GenBank, EMBL) using Biopython Bio.SeqIO.
GPTomics/bioSkills
Soft- or hard-clips PCR primer footprints from aligned amplicon BAMs so primer bases stop masquerading as confirmed reference sequence.
GPTomics/bioSkills
Filters BAM alignments by FLAG bits, mapping quality and regions with samtools view or pysam, with recipes for common keep and drop cases.
GPTomics/bioSkills
Create and use BAI/CSI indices for BAM/CRAM files using samtools and pysam.
Categories
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.