MCPInstall

nextflow-development

by anthropics OfficialMarketing & SEO

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

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Install

Works with every agent the skills CLI supports.

npx skills add https://github.com/anthropics/knowledge-work-plugins --skill nextflow-development
  1. 01

    Install

    Run the command in your project. The CLI asks which agents to install for.

  2. 02

    Describe the task

    Ask your agent for something this skill covers. It loads nextflow-development automatically when relevant.

  3. 03

    Review the result

    The agent follows the SKILL.md steps, scripts and references bundled with the skill.

SKILL.md

The instructions your agent reads when the skill is triggered.

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 outputs

Step 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.csv

DECISION 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.py

All critical checks must pass. If any fail, provide fix instructions:

Docker issues

ProblemFix
Not installedInstall from https://docs.docker.com/get-docker/
Permission deniedsudo usermod -aG docker $USER then re-login
Daemon not runningsudo systemctl start docker

Nextflow issues

ProblemFix
Not installedcurl -s https://get.nextflow.io | bash && mv nextflow ~/bin/
Version < 23.04nextflow self-update

Java issues

ProblemFix
Not installed / < 11sudo 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 TypePipelineVersionGoal
RNA-seqrnaseq3.22.2Gene expression
WGS/WESsarek3.7.1Variant calling
ATAC-seqatacseq2.1.2Chromatin accessibility

Auto-detect from data:

python scripts/detect_data_type.py /path/to/data

For pipeline-specific details:

  • references/pipelines/rnaseq.md
  • references/pipelines/sarek.md
  • references/pipelines/atacseq.md

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
PipelineCommand
rnaseqnextflow run nf-core/rnaseq -r 3.22.2 -profile test,docker --outdir test_rnaseq
sareknextflow run nf-core/sarek -r 3.7.1 -profile test,docker --outdir test_sarek
atacseqnextflow 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.log

If test fails, see references/troubleshooting.md.

Step 4: Create Samplesheet

Generate automatically

python scripts/generate_samplesheet.py /path/to/data <pipeline> -o samplesheet.csv

The 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,auto

sarek:

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,0

atacseq:

sample,fastq_1,fastq_2,replicate
CONTROL,/abs/path/ctrl_R1.fq.gz,/abs/path/ctrl_R2.fq.gz,1

Step 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:

  1. Genome: Which reference to use
  2. 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> \
    -resume

Key flags:

  • -r: Pin version
  • -profile docker: Use Docker (or singularity for 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.log

Key outputs by pipeline

rnaseq:

  • results/star_salmon/salmon.merged.gene_counts.tsv - Gene counts
  • results/star_salmon/salmon.merged.gene_tpm.tsv - TPM values

sarek:

  • results/variant_calling/*/ - VCF files
  • results/preprocessing/recalibrated/ - BAM files

atacseq:

  • results/macs2/narrowPeak/ - Peak calls
  • results/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> -resume

References

  • 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

FAQ

What does the nextflow-development skill do?

Run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) on sequencing data. Use when analyzing RNA-seq, WGS/WES, or ATAC-seq data—either local FASTQs or public datasets from GEO/SRA. Triggers on nf-core, Nextflow, FASTQ analysis, variant calling, gene expression, differential expression, GEO reanalysis, GSE/GSM/SRR accessions, or samplesheet creation.

How do I install nextflow-development?

Run npx skills add https://github.com/anthropics/knowledge-work-plugins --skill nextflow-development in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/nextflow-development/ (personal) or .claude/skills/nextflow-development/ (project).

Which AI agents can use nextflow-development?

Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.

Is nextflow-development safe to install?

No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.

Who maintains nextflow-development?

It is published by anthropics in anthropics/knowledge-work-plugins, and it is an official skill from the maker of the technology it covers. It has 2,380 recorded installs.

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