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.
Orchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it.
$ npx skills add GPTomics/bioSkills --skill bio-workflows-genome-assembly-pipeline -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install GPTomics/bioSkills bio-workflows-genome-assembly-pipeline --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/workflows/genome-assembly-pipeline .claude/skills/bio-workflows-genome-assembly-pipeline && 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-workflows-genome-assembly-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/genome-assembly-pipeline into .claude/skills/bio-workflows-genome-assembly-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-genome-assembly-pipeline", 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/workflows/genome-assembly-pipelineType 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-workflows-genome-assembly-pipeline -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install GPTomics/bioSkills bio-workflows-genome-assembly-pipeline --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/workflows/genome-assembly-pipeline .agents/skills/bio-workflows-genome-assembly-pipeline && 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-workflows-genome-assembly-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/genome-assembly-pipeline into .agents/skills/bio-workflows-genome-assembly-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-genome-assembly-pipeline", 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-workflows-genome-assembly-pipeline -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install GPTomics/bioSkills bio-workflows-genome-assembly-pipeline --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/workflows/genome-assembly-pipeline .cursor/skills/bio-workflows-genome-assembly-pipeline && 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-workflows-genome-assembly-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/genome-assembly-pipeline into .cursor/skills/bio-workflows-genome-assembly-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-genome-assembly-pipeline", 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 workflows/genome-assembly-pipeline--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-workflows-genome-assembly-pipeline -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install GPTomics/bioSkills bio-workflows-genome-assembly-pipeline --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/workflows/genome-assembly-pipeline .gemini/skills/bio-workflows-genome-assembly-pipeline && 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-workflows-genome-assembly-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/genome-assembly-pipeline into .gemini/skills/bio-workflows-genome-assembly-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-genome-assembly-pipeline", 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-workflows-genome-assembly-pipelineInstalls 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-workflows-genome-assembly-pipeline -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/workflows/genome-assembly-pipeline .github/skills/bio-workflows-genome-assembly-pipeline && 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-workflows-genome-assembly-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/genome-assembly-pipeline into .github/skills/bio-workflows-genome-assembly-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-genome-assembly-pipeline", 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-workflows-genome-assembly-pipeline -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-workflows-genome-assembly-pipeline --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/workflows/genome-assembly-pipeline .opencode/skills/bio-workflows-genome-assembly-pipeline && 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-workflows-genome-assembly-pipeline" agent skill from https://github.com/GPTomics/bioSkills/tree/main/workflows/genome-assembly-pipeline into .opencode/skills/bio-workflows-genome-assembly-pipeline/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "bio-workflows-genome-assembly-pipeline", 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-workflows-genome-assembly-pipelineOrchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it.
Bio Workflows Genome Assembly Pipeline is an agent skill from GPTomics/bioSkills. Orchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it. Profiles the genome first (k-mer spectrum - size, heterozygosity, ploidy), QCs reads, chooses an assembly path by data type (SPAdes for Illumina, Flye for noisy long reads, hifiasm for HiFi, metaFlye for communities), polishes only when needed, decontaminates, scaffolds with Hi-C, and finishes with three-axis QC (contiguity + completeness + correctness). Use when assembling a…
Its SKILL.md is about 4.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 3 other files (for example `examples/bacterial_assembly.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.
7 steps, taken from the step headings 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.
Shell commands in SKILL.md call:
python3shFrom 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 Workflows Genome Assembly Pipeline loads about 4.1k tokens when it runs. Until then it costs about 166 tokens; SKILL.md has 1,125 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). 1,125 words, ~4,147 tokens.
.claude/skills/bio-workflows-genome-assembly-pipeline/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: GenomeScope2 2.0+, meryl 1.4+, Merqury 1.3+, fastp 0.23+, SPAdes 4.0+, Flye 2.9+, hifiasm 0.25+, metaFlye 2.9+, Racon 1.5+, medaka 2.0+, minimap2 2.26+, FCS-GX 0.5+, CheckM2 1.0+, GUNC 1.0+, YaHS 1.2+, QUAST 5.2+, BUSCO 5.5+, samtools 1.19+. Each owning genome-assembly skill is the source of truth for its tool's pinned version.
Before using code patterns, verify installed versions match. If versions differ:
<tool> --version then <tool> --help to confirm flagsTool outputs are driven by more than the binary version: medaka consensus quality depends on the basecaller MODEL string (must match the basecaller, e.g. -m r1041_e82_400bps_sup_v5.0.0); BUSCO/compleasm results depend on the lineage dataset and OrthoDB generation (record them); CheckM2/GTDB-Tk results track the reference DATABASE release; hifiasm output filenames and default purge behaviour change across versions (verify against the installed build). If a command errors, introspect the installed tool and adapt rather than retrying.
"Assemble a genome from my sequencing reads and prove it is good" -> Profile the genome, QC reads, route to the right assembler by data type, polish only if needed, decontaminate, scaffold if Hi-C exists, and finish with three-axis QC. This skill ORCHESTRATES the genome-assembly category; it routes each step to the owning skill and encodes the cross-cutting decisions, not each tool's full option set.
A genome project fails when one number stands in for the whole. Profiling sets expectations (how big, how heterozygous, how many haplotypes) BEFORE assembling, the assembler answers contiguity, polishing answers per-base accuracy, decontamination answers provenance, scaffolding answers arrangement, and QC must independently address all three of contiguity, completeness, and correctness. The orchestration job is to keep these separate and route each to its skill: a high N50 says nothing about whether the bases are right (Merqury QV) or whether the sequence is the organism's (contamination), and skipping profiling means the assembler guesses the parameters that profiling would have set.
Raw reads (+ optional Hi-C, trio, short reads)
|
v
[0. Profile the genome] --> genome-assembly/genome-profiling
| k-mer spectrum (GenomeScope2) -> genome size, heterozygosity, ploidy.
| Sets NG50 denominator, expected haplotype count, hifiasm purge level,
| and which assembly path is even sensible. Do this BEFORE assembling.
v
[1. QC reads] -----------> short: read-qc/fastp-workflow
| long: long-read-sequencing/long-read-qc
| Garbage-in caps assembly quality; record platform + basecaller era
| (it is an assembly PARAMETER, see step 2), trim internal adapters.
v
[2. Choose path BY DATA TYPE]
| Illumina-only small/isolate -> genome-assembly/short-read-assembly (SPAdes)
| noisy ONT/CLR -> genome-assembly/long-read-assembly (Flye --nano-hq for R10)
| PacBio HiFi -> genome-assembly/hifi-assembly (hifiasm, phased)
| community sample -> genome-assembly/metagenome-assembly (metaFlye/metaSPAdes + binning)
| large/heterozygous euk -> long-read or HiFi, NOT short reads
v
[3. Polish IF needed] ---> genome-assembly/assembly-polishing
| noisy long-read assemblies: Racon -> medaka (model MUST match basecaller).
| Do NOT polish HiFi reflexively (often net-harmful). Measure with Merqury QV,
| not the reads polished with. Skip entirely for SPAdes/HiFi when QV is already high.
v
[4. Decontaminate] ------> genome-assembly/contamination-detection
| single organism: FCS-GX (GenBank-mandatory) + BlobToolKit blob plot.
| MAG: CheckM2 + GUNC (chimerism). Two disjoint problems (see below).
v
[5. Scaffold IF Hi-C] ---> genome-assembly/scaffolding
| automated YaHS produces a DRAFT; manual contact-map curation is the standard.
| Scaffold N50 != contig N50 (gaps are Ns). Skip if no Hi-C.
v
[6. Three-axis QC] ------> genome-assembly/assembly-qc
contiguity (auN/NG50 vs profiled size) + completeness (BUSCO/compleasm)
+ correctness (Merqury QV). Report the triad; NEVER N50 alone.| Scenario | Path | Routes to |
|---|---|---|
| Bacterial isolate, ONT R10 only | profile -> QC -> Flye --nano-hq -> medaka -> FCS-GX -> QC | long-read-assembly, assembly-polishing, contamination-detection |
| Bacterial isolate, Illumina only | profile -> fastp -> SPAdes --isolate -> FCS-GX -> QC | short-read-assembly |
| Small genome, ONT, max quality | profile -> QC -> multi-assembler consensus (Trycycler/Autocycler) -> medaka -> QC | long-read-assembly |
| Diploid eukaryote, HiFi (+Hi-C/trio) | profile -> QC -> hifiasm (hap1/hap2) -> purge check -> decontam -> scaffold -> QC | hifi-assembly, scaffolding, contamination-detection |
| Large heterozygous eukaryote, ONT | profile -> QC -> Flye -> purge_dups -> medaka -> decontam -> scaffold -> QC | long-read-assembly, scaffolding |
| Community / microbiome sample | QC -> metaFlye/metaSPAdes -> binning -> CheckM2 + GUNC | metagenome-assembly, contamination-detection |
| Hi-C reads available | after contigs+polish: scaffold, curate contact map | scaffolding |
| Reads not yet QC'd | start at step 1 | read-qc/fastp-workflow, long-read-sequencing/long-read-qc |
--nano-raw on R10/Dorado-SUP reads silently collapses real repeats while RAISING N50; --nano-hq is the R10 default. The platform + basecaller model is an assembly parameter, not metadata.Route the full treatment to genome-assembly/genome-profiling. The minimal orchestration step:
# k-mer count from ACCURATE reads (Illumina/HiFi, NEVER noisy ONT), then GenomeScope2 for size / heterozygosity / ploidy
meryl count k=21 output reads.meryl accurate_reads.fq.gz
meryl histogram reads.meryl > reads.hist
genomescope2 -i reads.hist -o gscope_out -k 21
# read off: estimated haploid genome size, heterozygosity %, and (with -p) ploidy.
# These set the NG50 denominator, the expected number of haplotypes, and the purge decision.Short reads route to read-qc/fastp-workflow; long reads to long-read-sequencing/long-read-qc.
fastp -i R1.fq.gz -I R2.fq.gz -o t_R1.fq.gz -O t_R2.fq.gz \
--detect_adapter_for_pe --qualified_quality_phred 20 --length_required 50 --html qc.htmlGive the assembler the exact preset for the chemistry; the wrong preset is silent. Detailed options live in the owning skills.
# Illumina-only small/isolate genome -> short-read-assembly
spades.py --isolate -1 t_R1.fq.gz -2 t_R2.fq.gz -o spades_out -t 16
# NOTE: --careful is small-genome-only; do NOT use it on large eukaryote genomes.
# Noisy ONT (R10/Dorado-SUP) -> long-read-assembly. --nano-hq is the modern default.
flye --nano-hq ont.fq.gz --out-dir flye_out --threads 16 # --genome-size optional in recent Flye
# PacBio HiFi -> hifi-assembly (phased by default; verify output filenames per version)
hifiasm -o asm -t 16 hifi.fq.gz # add --h1/--h2 (Hi-C) or -1/-2 (trio) to phase
# Community sample -> metagenome-assembly
flye --nano-hq ont.fq.gz --meta --out-dir metaflye_out --threads 16 # --meta is a modifier; still need a read-type selector. Then bin + CheckM2/GUNCPolishing is read-type-matched and conditional. Route to genome-assembly/assembly-polishing.
# Noisy long-read assembly: medaka with the MATCHING model. medaka_consensus does its own
# read-to-assembly alignment from -i/-d (no separate minimap2/BAM step needed); add a Racon
# round upstream only if the assembler did not already polish - see assembly-polishing.
medaka_consensus -i ont.fq.gz -d flye_out/assembly.fasta -o medaka_out -t 16 \
-m r1041_e82_400bps_sup_v5.0.0 # MUST match the basecaller model used to call the reads
# For a BACTERIAL isolate, prefer the methylation-aware bacterial model (medaka 2.0+):
# medaka_consensus -i ont.fq.gz -d assembly.fasta -o medaka_out --bacteriaDo NOT reflexively polish a HiFi assembly (already ~Q30+; over-polishing lowers QV). SPAdes output needs no separate long-read polish. The stop signal is a Merqury QV plateau, not a fixed iteration count, and the QV must be measured against reads independent of those used to polish.
# Single-organism assembly (GenBank-mandatory foreign screen + blob plot)
python3 ./fcs.py screen genome --fasta assembly.fa --out-dir gx_out/ --gx-db "$GXDB/gxdb" --tax-id <taxid>
# acts on EXCLUDE/TRIM/FIX cross-kingdom contigs; keep host-integrated foreign sequence (see contamination-detection)
# MAG (intra-domain contamination + chimerism)
checkm2 predict --input bins/ --output-directory checkm2_out --threads 16
gunc run --input_dir bins/ --out_dir gunc_out # chimerism, orthogonal to CheckM2YaHS produces a draft; the contact map is the QC, not decoration. Route to genome-assembly/scaffolding.
# Map Hi-C to contigs, then YaHS; inspect the contact map (PretextMap/Juicer) and break misjoins.
yahs assembly.fasta hic_to_contigs.bam -o yahs_out # output scaffolds + AGP; curate before publishingRoute the full treatment to genome-assembly/assembly-qc. Report all three axes; lead with the QV.
# Contiguity vs the PROFILED genome size (NG50/auN, not bare N50)
quast.py final.fasta -o quast_out -t 16 --est-ref-size <profiled_size>
# Completeness on the DEEPEST applicable clade (compleasm on good genomes; BUSCO otherwise)
busco -i final.fasta -l <clade>_odb10 -o busco_out -m genome -c 16
# Correctness: Merqury QV from ACCURATE reads (k from best_k.sh, not hardcoded)
K=$(sh $MERQURY/best_k.sh <genome_size_bp> | tail -n1 | awk '{print int($1+0.5)}') # round float->int
meryl count k=$K output reads.meryl accurate_reads.fq.gz
merqury.sh reads.meryl final.fasta merqury_out # QV + k-mer completeness + spectra-cn| Symptom | Cause | Fix |
|---|---|---|
| Assembly ~1.5-2x profiled size, high BUSCO-Duplicated | uncollapsed haplotigs (false duplication) | purge_dups; check half-coverage depth peak; do not over-purge real segmental duplications |
| Contiguous but gene models frameshift | noisy long-read assembly not polished | Racon -> medaka (matched model); measure QV |
| QV drops after polishing | over-polishing an already-accurate (HiFi) assembly | stop polishing; HiFi rarely needs short-read polish |
| medaka consensus worse than input | wrong basecaller model string | set -m to the model the reads were basecalled with |
| Fewer contigs than expected but repeats collapsed | --nano-raw used on R10 reads | re-run Flye with --nano-hq |
| CheckM2 says clean but bin looks mixed | chimera with disjoint markers | run GUNC; CheckM2 marker redundancy cannot see chimerism |
| Scaffold N50 huge, contig N50 small | scaffolding glue, not sequence | inspect contact map, break off-diagonal misjoins |
© 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 workflows/genome-assembly-pipeline 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 Workflows Genome Assembly Pipeline 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 Workflows Genome Assembly Pipeline this skillGPTomics/bioSkills | 1.2k | 1 repos | ~4.1k | 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
Orchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it. Bio Workflows Genome Assembly Pipeline is an agent skill from GPTomics/bioSkills. Orchestrates an end-to-end de novo genome assembly project, routing each step to the right genome-assembly skill rather than restating it.
Bio Workflows Genome Assembly Pipeline fits situations like: assembling a genome from raw reads and deciding which assembler; whether to polish; how to prove the result is good.
Run `npx skills add GPTomics/bioSkills --skill bio-workflows-genome-assembly-pipeline -a claude-code`. Or copy the skill folder (workflows/genome-assembly-pipeline in GPTomics/bioSkills) into .claude/skills/bio-workflows-genome-assembly-pipeline in your project. Claude Code loads it when a task matches its description.
Run `npx skills add GPTomics/bioSkills --skill bio-workflows-genome-assembly-pipeline -a codex`. Or copy the skill folder (workflows/genome-assembly-pipeline in GPTomics/bioSkills) into .agents/skills/bio-workflows-genome-assembly-pipeline 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-workflows-genome-assembly-pipeline -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-workflows-genome-assembly-pipeline, .gemini/skills/bio-workflows-genome-assembly-pipeline, .github/skills/bio-workflows-genome-assembly-pipeline and .opencode/skills/bio-workflows-genome-assembly-pipeline in your project.
Going by SKILL.md and its folder, Bio Workflows Genome Assembly Pipeline needs a shell for the scripts in its folder and the command-line tools its instructions call (python3 and sh). Our summary lists: Python 3; 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 Workflows Genome Assembly Pipeline is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.
About 4.1k tokens (SKILL.md is roughly 17k 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 Workflows Genome Assembly Pipeline: 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.