Nextflow Development

anthropics/knowledge-work-pluginsOfficial

This is a workflow wrapper that lets you run nf-core bioinformatics pipelines (rnaseq, sarek, atacseq) through Claude without writing pipeline code yourself. It handles the full stack: pulling datasets from GEO/SRA, environment checks, samplesheet generation, and pipeline execution with Docker. The checkpointed workflow is thoughtful, confirming genome selection and parameters before kicking off multi-hour jobs. It's aimed at bench scientists who need differential expression or variant calling but don't want to debug Nextflow configs. The implementation is explicitly marked as a prototype showing how to wrap one category of nf-core tools, not a production system, which is the right framing for something that automates compute-heavy analyses.

skills.sh installs: 2.4K
GitHub stars: 35.3K
last updated: Aug 21, 2026

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Install

$ npx -y skills add anthropics/knowledge-work-plugins --skill nextflow-development --agent claude-code

Installs into .claude/skills of the current project.

Files

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

SKILL.md on GitHub