Nextflow Development
AnthropicOfficialPublished by Anthropic
- Skill
- Workflow
- Science and research
- Apache-2.0
- Medium risk
- Has scripts
AutoGineer summary
Written by AutoGineer from the skill’s own files, not by the author
This skill provides a structured workflow for researchers to execute nf-core bioinformatics pipelines. It guides users through environment validation, data acquisition from public repositories like GEO/SRA or local sources, samplesheet generation, and pipeline execution. It supports standard omics analyses including gene expression, variant calling, and chromatin accessibility, while ensuring the computing environment is correctly configured with Docker and Nextflow.
What it does
- Validates system requirements including Docker, Nextflow, and Java versions.
- Fetches and prepares public sequencing data from GEO/SRA accessions.
- Automates the creation and validation of pipeline-specific samplesheets.
- Executes nf-core pipelines for RNA-seq, Sarek, and ATAC-seq workflows.
- Manages reference genome downloads and configuration.
- Verifies analysis outputs and pipeline completion logs.
When to use it
- When you need to perform large-scale omics analysis on local FASTQ files.
- When reanalyzing public datasets from GEO/SRA.
- When you require a standardized, reproducible pipeline for RNA-seq, WGS/WES, or ATAC-seq.
- When setting up a new bioinformatics environment for nf-core pipeline execution.
Advantages
- Standardizes bioinformatics workflows for non-specialists.
- Reduces manual errors in samplesheet creation and environment setup.
- Provides clear decision points for pipeline selection and configuration.
- Includes built-in validation steps to ensure pipeline success.
- Simplifies the integration of public data into local analysis pipelines.
Usage examples
- “Run the rnaseq pipeline on my local FASTQ files.”
- The agent will check your environment, help you generate a samplesheet, confirm the genome, and execute the nf-core/rnaseq pipeline.
- “Analyze the public dataset GSE110004.”
- The agent will fetch the study metadata, download the relevant FASTQ files, create the necessary samplesheet, and prepare the pipeline for execution.
Install
Download the skill as a ZIP:
https://mp-staging.autogineer.com/agent-skills/anthropics/nextflow-development/downloadUnzip it into the cross-client skills folder of your project:
mkdir -p .agents/skills && unzip nextflow-development.zip -d .agents/skills
Documentation by Anthropic
nf-core Pipeline Deployment
Run nf-core bioinformatics pipelines on local or public sequencing data.
Target users: Bench scientists and researchers without specialized bioinformatics training who need to run large-scale omics analyses—differential expression, variant calling, or chromatin accessibility analysis.
Workflow Checklist
- [ ] Step 0: Acquire data (if from GEO/SRA)
- [ ] Step 1: Environment check (MUST pass)
- [ ] Step 2: Select pipeline (confirm with user)
- [ ] Step 3: Run test profile (MUST pass)
- [ ] Step 4: Create samplesheet
- [ ] Step 5: Configure & run (confirm genome with user)
- [ ] Step 6: Verify outputsStep 0: Acquire Data (GEO/SRA Only)
Skip this step if user has local FASTQ files.
For public datasets, fetch from GEO/SRA first. See references/geo-sra-acquisition.md for the full workflow.
Quick start:
# 1. Get study info
python scripts/sra_geo_fetch.py info GSE110004
# 2. Download (interactive mode)
python scripts/sra_geo_fetch.py download GSE110004 -o ./fastq -i
# 3. Generate samplesheet
python scripts/sra_geo_fetch.py samplesheet GSE110004 --fastq-dir ./fastq -o samplesheet.csvDECISION POINT: After fetching study info, confirm with user:
Which sample subset to download (if multiple data types)
Suggested genome and pipeline
Then continue to Step 1.
Step 1: Environment Check
Run first. Pipeline will fail without passing environment.
python scripts/check_environment.pyAll critical checks must pass. If any fail, provide fix instructions:
Docker issues
| Problem | Fix |
|---|---|
| Not installed | Install from https://docs.docker.com/get-docker/ |
| Permission denied | sudo usermod -aG docker $USER then re-login |
| Daemon not running | sudo systemctl start docker |
Nextflow issues
| Problem | Fix |
|---|---|
| Not installed | curl -s https://get.nextflow.io | bash && mv nextflow ~/bin/ |
| Version < 23.04 | nextflow self-update |
Java issues
| Problem | Fix |
|---|---|
| Not installed / < 11 | sudo apt install openjdk-11-jdk |
Do not proceed until all checks pass. For HPC/Singularity, see references/troubleshooting.md.
Step 2: Select Pipeline
DECISION POINT: Confirm with user before proceeding.
| Data Type | Pipeline | Version | Goal |
|---|---|---|---|
| RNA-seq | rnaseq | 3.22.2 | Gene expression |
| WGS/WES | sarek | 3.7.1 | Variant calling |
| ATAC-seq | atacseq | 2.1.2 | Chromatin accessibility |
Auto-detect from data:
python scripts/detect_data_type.py /path/to/dataFor pipeline-specific details:
Step 3: Run Test Profile
Validates environment with small data. MUST pass before real data.
nextflow run nf-core/<pipeline> -r <version> -profile test,docker --outdir test_output| Pipeline | Command |
|---|---|
| rnaseq | nextflow run nf-core/rnaseq -r 3.22.2 -profile test,docker --outdir test_rnaseq |
| sarek | nextflow run nf-core/sarek -r 3.7.1 -profile test,docker --outdir test_sarek |
| atacseq | nextflow run nf-core/atacseq -r 2.1.2 -profile test,docker --outdir test_atacseq |
Verify:
ls test_output/multiqc/multiqc_report.html
grep "Pipeline completed successfully" .nextflow.logIf test fails, see references/troubleshooting.md.
Step 4: Create Samplesheet
Generate automatically
python scripts/generate_samplesheet.py /path/to/data <pipeline> -o samplesheet.csvThe script:
Discovers FASTQ/BAM/CRAM files
Pairs R1/R2 reads
Infers sample metadata
Validates before writing
For sarek: Script prompts for tumor/normal status if not auto-detected.
Validate existing samplesheet
python scripts/generate_samplesheet.py --validate samplesheet.csv <pipeline>Samplesheet formats
rnaseq:
sample,fastq_1,fastq_2,strandedness
SAMPLE1,/abs/path/R1.fq.gz,/abs/path/R2.fq.gz,autosarek:
patient,sample,lane,fastq_1,fastq_2,status
patient1,tumor,L001,/abs/path/tumor_R1.fq.gz,/abs/path/tumor_R2.fq.gz,1
patient1,normal,L001,/abs/path/normal_R1.fq.gz,/abs/path/normal_R2.fq.gz,0atacseq:
sample,fastq_1,fastq_2,replicate
CONTROL,/abs/path/ctrl_R1.fq.gz,/abs/path/ctrl_R2.fq.gz,1Step 5: Configure & Run
5a. Check genome availability
python scripts/manage_genomes.py check <genome>
# If not installed:
python scripts/manage_genomes.py download <genome>Common genomes: GRCh38 (human), GRCh37 (legacy), GRCm39 (mouse), R64-1-1 (yeast), BDGP6 (fly)
5b. Decision points
DECISION POINT: Confirm with user:
Genome: Which reference to use
Pipeline-specific options:
rnaseq: aligner (star_salmon recommended, hisat2 for low memory)
sarek: tools (haplotypecaller for germline, mutect2 for somatic)
atacseq: read_length (50, 75, 100, or 150)
5c. Run pipeline
nextflow run nf-core/<pipeline> \
-r <version> \
-profile docker \
--input samplesheet.csv \
--outdir results \
--genome <genome> \
-resumeKey flags:
-r: Pin version-profile docker: Use Docker (orsingularityfor HPC)--genome: iGenomes key-resume: Continue from checkpoint
Resource limits (if needed):
--max_cpus 8 --max_memory '32.GB' --max_time '24.h'Step 6: Verify Outputs
Check completion
ls results/multiqc/multiqc_report.html
grep "Pipeline completed successfully" .nextflow.logKey outputs by pipeline
rnaseq:
results/star_salmon/salmon.merged.gene_counts.tsv- Gene countsresults/star_salmon/salmon.merged.gene_tpm.tsv- TPM values
sarek:
results/variant_calling/*/- VCF filesresults/preprocessing/recalibrated/- BAM files
atacseq:
results/macs2/narrowPeak/- Peak callsresults/bwa/mergedLibrary/bigwig/- Coverage tracks
Quick Reference
For common exit codes and fixes, see references/troubleshooting.md.
Resume failed run
nextflow run nf-core/<pipeline> -resumeReferences
references/geo-sra-acquisition.md - Downloading public GEO/SRA data
references/troubleshooting.md - Common issues and fixes
references/installation.md - Environment setup
references/pipelines/rnaseq.md - RNA-seq pipeline details
references/pipelines/sarek.md - Variant calling details
references/pipelines/atacseq.md - ATAC-seq details
Disclaimer
This skill is provided as a prototype example demonstrating how to integrate nf-core bioinformatics pipelines into Claude Code for automated analysis workflows. The current implementation supports three pipelines (rnaseq, sarek, and atacseq), serving as a foundation that enables the community to expand support to the full set of nf-core pipelines.
It is intended for educational and research purposes and should not be considered production-ready without appropriate validation for your specific use case. Users are responsible for ensuring their computing environment meets pipeline requirements and for verifying analysis results.
Anthropic does not guarantee the accuracy of bioinformatics outputs, and users should follow standard practices for validating computational analyses. This integration is not officially endorsed by or affiliated with the nf-core community.
Attribution
When publishing results, cite the appropriate pipeline. Citations are available in each nf-core repository's CITATIONS.md file (e.g., https://github.com/nf-core/rnaseq/blob/3.22.2/CITATIONS.md).
Licenses
nf-core pipelines: MIT License (https://nf-co.re/about)
Nextflow: Apache License, Version 2.0 (https://www.nextflow.io/about-us.html)
NCBI SRA Toolkit: Public Domain (https://github.com/ncbi/sra-tools/blob/master/LICENSE)
Files
- ATTRIBUTION.mdAdded by AutoGineer972 B
- LICENSE.txt10.0 KiB
- SKILL.md8.6 KiB
- references/geo-sra-acquisition.md13.8 KiB
- references/installation.md1.8 KiB
- references/pipelines/atacseq.md4.1 KiB
- references/pipelines/rnaseq.md3.5 KiB
- references/pipelines/sarek.md3.9 KiB
- references/troubleshooting.md3.3 KiB
- scripts/check_environment.pyScript13.8 KiB
- scripts/config/genomes.yaml3.4 KiB
- scripts/config/pipelines/atacseq.yaml5.0 KiB
- scripts/config/pipelines/rnaseq.yaml4.1 KiB
- scripts/config/pipelines/sarek.yaml6.1 KiB
- scripts/detect_data_type.pyScript9.7 KiB
- scripts/generate_samplesheet.pyScript15.1 KiB
- scripts/manage_genomes.pyScript16.3 KiB
- scripts/sra_geo_fetch.pyScript24.4 KiB
- scripts/utils/__init__.pyScript1.7 KiB
- scripts/utils/file_discovery.pyScript5.0 KiB
- scripts/utils/ncbi_utils.pyScript27.8 KiB
- scripts/utils/sample_inference.pyScript7.7 KiB
- scripts/utils/validators.pyScript8.2 KiB
scripts/manage_genomes.py
#!/usr/bin/env python3
"""
Genome reference management for nf-core pipelines.
Manages downloading, caching, and accessing genome references from iGenomes.
Supports auto-download when references aren't available locally.
Usage:
python manage_genomes.py list
python manage_genomes.py check GRCh38
python manage_genomes.py download GRCh38
python manage_genomes.py params GRCh38
"""
import argparse
import json
import os
import subprocess
import sys
from pathlib import Path
from typing import Dict, List, Optional
# iGenomes reference configuration
IGENOMES = {
# Human
'GRCh38': {
'display_name': 'Human GRCh38/hg38',
'species': 'Homo sapiens',
'aliases': ['hg38', 'GRCh38.p14'],
's3_base': 's3://ngi-igenomes/igenomes/Homo_sapiens/NCBI/GRCh38',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
'bwa_index': 'Sequence/BWAIndex/',
'star_index': 'Sequence/STARIndex/',
}
},
'GRCh37': {
'display_name': 'Human GRCh37/hg19',
'species': 'Homo sapiens',
'aliases': ['hg19', 'GRCh37.p13'],
's3_base': 's3://ngi-igenomes/igenomes/Homo_sapiens/NCBI/GRCh37',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
'bwa_index': 'Sequence/BWAIndex/',
'star_index': 'Sequence/STARIndex/',
}
},
# Mouse
'GRCm39': {
'display_name': 'Mouse GRCm39/mm39',
'species': 'Mus musculus',
'aliases': ['mm39', 'GRCm39'],
's3_base': 's3://ngi-igenomes/igenomes/Mus_musculus/Ensembl/GRCm39',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
'bwa_index': 'Sequence/BWAIndex/',
'star_index': 'Sequence/STARIndex/',
}
},
'GRCm38': {
'display_name': 'Mouse GRCm38/mm10',
'species': 'Mus musculus',
'aliases': ['mm10', 'GRCm38'],
's3_base': 's3://ngi-igenomes/igenomes/Mus_musculus/NCBI/GRCm38',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
'bwa_index': 'Sequence/BWAIndex/',
'star_index': 'Sequence/STARIndex/',
}
},
# Yeast
'R64-1-1': {
'display_name': 'Yeast R64-1-1/sacCer3',
'species': 'Saccharomyces cerevisiae',
'aliases': ['sacCer3', 'S288C', 'yeast'],
's3_base': 's3://ngi-igenomes/igenomes/Saccharomyces_cerevisiae/Ensembl/R64-1-1',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
'bwa_index': 'Sequence/BWAIndex/',
'star_index': 'Sequence/STARIndex/',
}
},
# Fruit fly
'BDGP6': {
'display_name': 'Drosophila BDGP6/dm6',
'species': 'Drosophila melanogaster',
'aliases': ['dm6', 'BDGP6', 'fly'],
's3_base': 's3://ngi-igenomes/igenomes/Drosophila_melanogaster/Ensembl/BDGP6',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
}
},
# C. elegans
'WBcel235': {
'display_name': 'C. elegans WBcel235/ce11',
'species': 'Caenorhabditis elegans',
'aliases': ['ce11', 'worm'],
's3_base': 's3://ngi-igenomes/igenomes/Caenorhabditis_elegans/Ensembl/WBcel235',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
'bwa_index': 'Sequence/BWAIndex/',
'star_index': 'Sequence/STARIndex/',
}
},
# Zebrafish
'GRCz11': {
'display_name': 'Zebrafish GRCz11/danRer11',
'species': 'Danio rerio',
'aliases': ['danRer11', 'zebrafish'],
's3_base': 's3://ngi-igenomes/igenomes/Danio_rerio/Ensembl/GRCz11',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
'bwa_index': 'Sequence/BWAIndex/',
'star_index': 'Sequence/STARIndex/',
}
},
'GRCz10': {
'display_name': 'Zebrafish GRCz10/danRer10',
'species': 'Danio rerio',
'aliases': ['danRer10'],
's3_base': 's3://ngi-igenomes/igenomes/Danio_rerio/Ensembl/GRCz10',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
}
},
# Rat
'Rnor_6.0': {
'display_name': 'Rat Rnor_6.0/rn6',
'species': 'Rattus norvegicus',
'aliases': ['rn6', 'Rnor6', 'rat'],
's3_base': 's3://ngi-igenomes/igenomes/Rattus_norvegicus/Ensembl/Rnor_6.0',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
'bwa_index': 'Sequence/BWAIndex/',
'star_index': 'Sequence/STARIndex/',
}
},
# Arabidopsis
'TAIR10': {
'display_name': 'Arabidopsis TAIR10',
'species': 'Arabidopsis thaliana',
'aliases': ['arabidopsis'],
's3_base': 's3://ngi-igenomes/igenomes/Arabidopsis_thaliana/Ensembl/TAIR10',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
'bwa_index': 'Sequence/BWAIndex/',
'star_index': 'Sequence/STARIndex/',
}
},
# Chicken
'GRCg6a': {
'display_name': 'Chicken GRCg6a/galGal6',
'species': 'Gallus gallus',
'aliases': ['galGal6', 'chicken'],
's3_base': 's3://ngi-igenomes/igenomes/Gallus_gallus/Ensembl/GRCg6a',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
}
},
# Dog
'CanFam3.1': {
'display_name': 'Dog CanFam3.1/canFam3',
'species': 'Canis lupus familiaris',
'aliases': ['canFam3', 'dog'],
's3_base': 's3://ngi-igenomes/igenomes/Canis_familiaris/Ensembl/CanFam3.1',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
}
},
# Pig
'Sscrofa11.1': {
'display_name': 'Pig Sscrofa11.1/susScr11',
'species': 'Sus scrofa',
'aliases': ['susScr11', 'pig'],
's3_base': 's3://ngi-igenomes/igenomes/Sus_scrofa/Ensembl/Sscrofa11.1',
'files': {
'fasta': 'Sequence/WholeGenomeFasta/genome.fa',
'gtf': 'Annotation/Genes/genes.gtf',
}
},
}
def get_cache_dir() -> Path:
"""Get genome cache directory."""
cache_dir = os.environ.get(
'NF_CORE_GENOME_CACHE',
os.path.expanduser('~/.nf-core/genomes')
)
return Path(cache_dir)
def resolve_genome_id(genome: str) -> Optional[str]:
"""Resolve genome ID from name or alias."""
# Direct match
if genome in IGENOMES:
return genome
# Check aliases
genome_lower = genome.lower()
for gid, info in IGENOMES.items():
if genome_lower in [a.lower() for a in info.get('aliases', [])]:
return gid
return None
def is_genome_installed(genome_id: str) -> bool:
"""Check if genome is installed locally."""
cache_dir = get_cache_dir()
genome_dir = cache_dir / genome_id
# Check for fasta as minimum requirement
fasta_path = genome_dir / 'genome.fa'
return fasta_path.exists()
def get_genome_path(genome_id: str) -> Optional[Path]:
"""Get local path to genome if installed."""
if not is_genome_installed(genome_id):
return None
return get_cache_dir() / genome_id
def list_genomes(installed_only: bool = False) -> List[Dict]:
"""List available genomes."""
result = []
for genome_id, info in IGENOMES.items():
installed = is_genome_installed(genome_id)
if installed_only and not installed:
continue
genome_path = get_genome_path(genome_id) if installed else None
result.append({
'id': genome_id,
'display_name': info['display_name'],
'species': info['species'],
'aliases': info.get('aliases', []),
'installed': installed,
'path': str(genome_path) if genome_path else None,
})
return result
def download_genome(
genome_id: str,
components: Optional[List[str]] = None,
force: bool = False
) -> bool:
"""
Download genome reference files from iGenomes.
Args:
genome_id: Genome identifier (e.g., GRCh38)
components: Specific components to download (fasta, gtf, etc.)
force: Overwrite existing files
Returns:
True if successful
"""
# Resolve genome ID
resolved = resolve_genome_id(genome_id)
if not resolved:
print(f"Unknown genome: {genome_id}")
print(f"Available: {', '.join(IGENOMES.keys())}")
return False
genome_id = resolved
info = IGENOMES[genome_id]
# Check for AWS CLI
aws_available = subprocess.run(
['which', 'aws'],
capture_output=True
).returncode == 0
if not aws_available:
print("AWS CLI not found. Required for iGenomes download.")
print("Install with: pip install awscli")
print("\nAlternative: Use --genome flag with nf-core pipelines")
print("which will auto-download references (slower, per-run).")
return False
# Create cache directory
cache_dir = get_cache_dir()
genome_dir = cache_dir / genome_id
genome_dir.mkdir(parents=True, exist_ok=True)
# Determine components to download
if components is None:
components = ['fasta', 'gtf'] # Minimum required
print(f"Downloading {info['display_name']} to {genome_dir}")
print(f"Components: {', '.join(components)}")
success = True
for component in components:
if component not in info.get('files', {}):
print(f" Skipping {component}: not available for {genome_id}")
continue
remote_path = info['files'][component]
s3_path = f"{info['s3_base']}/{remote_path}"
# Determine local path
if remote_path.endswith('/'):
# Directory (e.g., index)
local_path = genome_dir / component
else:
# File
filename = Path(remote_path).name
local_path = genome_dir / filename
if local_path.exists() and not force:
print(f" {component}: Already exists (use --force to overwrite)")
continue
print(f" Downloading {component}...")
# Build AWS command
cmd = ['aws', 's3', 'cp', '--no-sign-request']
if remote_path.endswith('/'):
cmd.extend(['--recursive', s3_path, str(local_path)])
else:
cmd.extend([s3_path, str(local_path)])
result = subprocess.run(cmd, capture_output=True, text=True)
if result.returncode != 0:
print(f" ERROR downloading {component}:")
print(f" {result.stderr[:200]}")
success = False
else:
print(f" {component}: Downloaded successfully")
if success:
print(f"\nGenome {genome_id} ready at: {genome_dir}")
else:
print(f"\nSome components failed to download.")
return success
def get_nextflow_params(genome_id: str) -> Dict[str, str]:
"""
Get Nextflow parameters for a genome.
Returns dict with --fasta, --gtf if local,
or just --genome if using iGenomes key.
"""
resolved = resolve_genome_id(genome_id)
if not resolved:
return {'error': f'Unknown genome: {genome_id}'}
genome_id = resolved
# Check if installed locally
genome_path = get_genome_path(genome_id)
if genome_path:
params = {}
# Check for local files
fasta = genome_path / 'genome.fa'
if fasta.exists():
params['fasta'] = str(fasta)
gtf = genome_path / 'genes.gtf'
if gtf.exists():
params['gtf'] = str(gtf)
if params:
return params
# Fall back to iGenomes key
return {'genome': genome_id}
def print_genome_list(genomes: List[Dict], output_json: bool = False):
"""Print genome list."""
if output_json:
print(json.dumps(genomes, indent=2))
return
print("\n" + "=" * 50)
print(" Available Genomes")
print("=" * 50 + "\n")
for g in genomes:
status = "\033[92m[installed]\033[0m" if g['installed'] else ""
print(f" {g['id']}: {g['display_name']} {status}")
print(f" Species: {g['species']}")
print(f" Aliases: {', '.join(g['aliases'])}")
if g['path']:
print(f" Path: {g['path']}")
print()
def main():
parser = argparse.ArgumentParser(
description='Manage genome references for nf-core pipelines',
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog="""
Commands:
list List available genomes
check <genome> Check if genome is installed
download <genome> Download genome from iGenomes
params <genome> Get Nextflow parameters for genome
Examples:
%(prog)s list
%(prog)s list --installed
%(prog)s check GRCh38
%(prog)s download GRCh38
%(prog)s download GRCh38 --components fasta gtf star_index
%(prog)s params GRCh38
"""
)
subparsers = parser.add_subparsers(dest='command', help='Commands')
# List command
list_parser = subparsers.add_parser('list', help='List available genomes')
list_parser.add_argument('--installed', action='store_true',
help='Show only installed genomes')
list_parser.add_argument('--json', action='store_true',
help='Output as JSON')
# Check command
check_parser = subparsers.add_parser('check', help='Check if genome is installed')
check_parser.add_argument('genome', help='Genome ID (e.g., GRCh38)')
check_parser.add_argument('--json', action='store_true',
help='Output as JSON')
# Download command
dl_parser = subparsers.add_parser('download', help='Download genome from iGenomes')
dl_parser.add_argument('genome', help='Genome ID (e.g., GRCh38)')
dl_parser.add_argument('--components', nargs='+',
help='Specific components (fasta, gtf, bwa_index, star_index)')
dl_parser.add_argument('--force', action='store_true',
help='Overwrite existing files')
# Params command
params_parser = subparsers.add_parser('params', help='Get Nextflow params for genome')
params_parser.add_argument('genome', help='Genome ID')
params_parser.add_argument('--json', action='store_true',
help='Output as JSON')
args = parser.parse_args()
if args.command == 'list':
genomes = list_genomes(installed_only=args.installed)
print_genome_list(genomes, args.json)
elif args.command == 'check':
resolved = resolve_genome_id(args.genome)
if not resolved:
print(f"Unknown genome: {args.genome}")
sys.exit(1)
installed = is_genome_installed(resolved)
path = get_genome_path(resolved) if installed else None
if args.json:
print(json.dumps({
'genome': resolved,
'installed': installed,
'path': str(path) if path else None
}))
else:
if installed:
print(f"✓ Genome {resolved} is installed at: {path}")
else:
print(f"✗ Genome {resolved} is not installed locally")
print(f" Download with: python {sys.argv[0]} download {resolved}")
sys.exit(0 if installed else 1)
elif args.command == 'download':
success = download_genome(args.genome, args.components, args.force)
sys.exit(0 if success else 1)
elif args.command == 'params':
params = get_nextflow_params(args.genome)
if args.json:
print(json.dumps(params))
else:
if 'error' in params:
print(f"Error: {params['error']}")
sys.exit(1)
for key, value in params.items():
print(f"--{key} {value}")
else:
parser.print_help()
sys.exit(1)
if __name__ == '__main__':
main()
Requirements and compatibility
Written for: Claude Code, Claude apps
Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta
“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.
Needs: Needs network access, Needs Python
Security
What our scanner noticed· Medium risk
- Instructions that use the network
- Downloads piped into a shell
- Scripts that your agent may run
This skill includes scripts that your agent may run. Read them before you use it.
A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.
Attribution and licence
- Published by
- Anthropic
- Publisher
- Anthropic (Official)
- Source
- anthropics/knowledge-work-plugins · bio-research/skills/nextflow-development · commit e050c2e
- Licence
- Apache License 2.0 (redistribution allowed)
- ATTRIBUTION.md (written by AutoGineer)
- Synced
AutoGineer is not affiliated with or endorsed by Anthropic.
Download ZIP
The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download nextflow-development.zip