nextflow-workflow-engine
Dataflow workflow engine for scalable bioinformatics pipelines. Defines processes (containerized tasks) connected by channels; runs local, HPC (SLURM/SGE), cloud (AWS/GCP/Azure), or Kubernetes via a single config change. Powers nf-core. Use Snakemake for rule-based Python workflows; use Nextflow for containerized, cloud-native, and nf-core pipelines.
npx skills add BioTender-max/awesome-bio-agent-skills --skill nextflow-workflow-engine --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Nextflow — Scalable Scientific Workflow Engine ## Overview Nextflow implements a dataflow programming model where **processes** (containerized execution units) consume and emit data through **channels** (asynchronous queues). This design enables implicit parallelization — processes run as soon as their input channels have data, without manual dependency management. Nextflow handles process orchestration across local machines, HPC clusters (SLURM, SGE, PBS), and cloud platforms (AWS Batch, Google Cloud Life Sciences, Azure Batch) by swapping a single configuration profile. The nf-core community provides 100+ validated Nextflow pipelines (RNA-seq, WGS, ChIP-seq, scRNA-seq) following best practices with automated testing. ## When to Use - Building containerized bioinformatics pipelines that must run on HPC, AWS, and local environments without code changes - Using nf-core community pipelines (nf-core/rnaseq, nf-core/sarek, nf-core/chipseq) out of the box - Processing thousands of samples with implicit parallelization across a SLURM cluster - Writing pipelines where each step runs inside a Docker or Singularity container for reproducibility - Monitoring pipeline execution and resuming
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Processes — Containerized Task Units
- Module 2: Channels — Data Queues Between Processes
- Module 3: Workflow Block — Pipeline DAG Definition
- Module 4: Configuration — Profiles for Different Environments
- Module 5: Operators — Channel Transformations
- Module 6: Error Handling and Resuming
- Key Parameters
- Common Workflows
- Workflow 1: Complete RNA-seq Pipeline (nf-core/rnaseq)
Install Nextflow (self-contained JAR — no sudo required) curl -s https://get.nextflow.io | bash chmod +x nextflow export PATH="$PWD:$PATH" Verify nextflow -version Nextflow version 24.10.1 Install nf-core tools (Python) pip install nf-core Pull an nf-core pipeline
What does the nextflow-workflow-engine skill do?
Dataflow workflow engine for scalable bioinformatics pipelines. Defines processes (containerized tasks) connected by channels; runs local, HPC (SLURM/SGE), cloud (AWS/GCP/Azure), or Kubernetes via a single config change. Powers nf-core. Use Snakemake for rule-based Python workflows; use Nextflow for containerized, cloud-native, and nf-core pipelines.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill nextflow-workflow-engine --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.