
Nextflow
FreeBuild and run reproducible data pipelines with ease.
Free · Opens the source repo
What Nextflow does
Nextflow is a powerful workflow language and runtime designed for building reproducible, portable, and scalable data pipelines. It is particularly prevalent in bioinformatics but is versatile enough to be applied to any data-intensive computation. The core of Nextflow's functionality lies in its dataflow programming model, which allows users to define processes that are linked by channels. This means that the execution order and parallelism are automatically inferred based on data dependencies, eliminating the need for an explicit scheduler.
One of the key advantages of Nextflow is its ability to run the same pipeline across different environments—whether locally, on high-performance computing (HPC) systems, or in the cloud—simply by changing configuration profiles rather than modifying the code. This flexibility is crucial for researchers and developers who need to ensure that their workflows are reproducible and can be executed in various settings. Additionally, the integration of containerization technologies like Docker, Singularity, and Conda enhances reproducibility by encapsulating the execution environment.
This skill is particularly beneficial for users who want to run existing nf-core pipelines or develop their own Nextflow scripts. It provides comprehensive support for writing and testing nf-core-compliant modules and subworkflows, configuring executors and containers, and scaling workflows to cloud or HPC environments. The skill also includes detailed references for running pipelines, developing new workflows, and testing modules, making it a valuable resource for both beginners and advanced users in the scientific and bioinformatics communities.
Overall, the Nextflow skill is a robust tool for anyone involved in data-heavy scientific workflows, enabling them to streamline their processes and ensure that their research is reproducible and scalable.
When to use it
Use this skill when you need to run existing nf-core or custom Nextflow pipelines, or when you want to develop new workflows or modules.
When not to use it
This skill may not be suitable for users who are not working with data pipelines or who require a different workflow management system.
What you can build with it
Running nf-core Pipelines
Quickly run established nf-core pipelines by specifying the pipeline name and desired configuration profile.
Developing Custom Workflows
Create new Nextflow workflows or modify existing ones using the skill's references for language and development.
Scaling Workflows to Cloud or HPC
Effortlessly scale your workflows to cloud platforms or HPC environments by configuring executors and containers.
How to install Nextflow
View source1. Install with the skills CLI
npx skills add k-dense-ai/scientific-agent-skills/nextflow --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by k-dense-aiNextflow
Overview
Nextflow is a workflow language and runtime for building reproducible, portable, scalable data pipelines. It is dominant in bioinformatics but works for any data-heavy computation. nf-core is a community curating production-grade Nextflow pipelines, reusable modules, and the nf-core tooling on top of Nextflow.
Key ideas:
- Dataflow programming: pipelines are
processtasks connected by channels. Nextflow infers execution order and parallelism from data dependencies — there is no explicit scheduler to write. - Write once, run anywhere: the same pipeline runs locally, on HPC (SLURM, SGE, LSF, PBS), and on cloud (AWS Batch, Google Batch, Azure Batch, Kubernetes) by changing config/profiles, not code.
- Reproducibility: per-task containers (Docker/Singularity/Apptainer/Conda/Wave) +
-resumecaching + pinned pipeline revisions. - DSL2 is the modern, required syntax: modular
process/workflow/includedefinitions.
This skill covers both running existing pipelines and developing your own (Nextflow language + nf-core conventions, testing with nf-test, configuration, and deployment).
When to Use This Skill
Use this skill when the user wants to:
- Run an nf-core or custom Nextflow pipeline, or debug a failing/resuming run.
- Write or modify
.nfscripts,nextflow.config, profiles, ornextflow_schema.json. - Author or test nf-core-style modules/subworkflows (
main.nf,meta.yml,tests/, nf-test). - Configure executors, containers, or resources; scale to HPC or cloud.
- Build a reproducible scientific/bioinformatics workflow (even if "Nextflow" is not named).
- Understand processes, channels, operators,
take/emit,publishDir,ext.args, meta maps.
Setup
Nextflow needs Bash and Java 17 or newer (17–25 supported). Verify with java -version.
# Install Nextflow (self-contained launcher)
curl -s https://get.nextflow.io | bash # creates ./nextflow
sudo mv nextflow /usr/local/bin/ # put on PATH
nextflow info # verify
# Or via conda/bioconda (also gets a managed Java)
conda create -n nf -c bioconda -c conda-forge nextflow nf-core
# nf-core tools (Python) for creating/linting/running nf-core assets
uv pip install nf-core # or: conda install -c bioconda nf-core
nf-core --version
Pin the engine for reproducibility: export NXF_VER=24.10.0 (use an [edge] release only if needed). For air-gapped/HPC, see references/running-pipelines.md (offline mode) and references/configuration.md.
Two Modes of Work
Decide which path the user is on — it changes everything:
| Goal | Start here |
|---|---|
Run an existing pipeline (nf-core or a .nf you were given) | references/running-pipelines.md |
| Develop a new pipeline / module / subworkflow | references/language.md + references/developing.md |
| Configure / scale (HPC, cloud, containers, resources) | references/configuration.md + references/containers.md |
| Test modules/pipelines | references/testing.md |
Quick Start
Run an nf-core pipeline
Always smoke-test with the bundled test profile first; it uses tiny data and proves your environment works.
# 1. Confirm setup works (downloads pipeline + tiny test data)
nextflow run nf-core/rnaseq -profile test,docker --outdir results
# 2. Real run: pin a revision (-r), pick a container engine, pass inputs
nextflow run nf-core/rnaseq -r 3.14.0 \
-profile docker \
--input samplesheet.csv \
--genome GRCh38 \
--outdir results \
-resume
-profile(single dash) selects bundled config profiles; combine them comma-separated, e.g.test,docker. Container/infra profiles (docker,singularity,conda) are mutually exclusive — pick one.--input,--genome,--outdir(double dash) are pipeline parameters. nf-core pipelines take a samplesheet CSV, not loose files.-resumereuses cached results from the last run.-r <version>pins a release for reproducibility.
Use nf-core pipelines launch <name> for an interactive, schema-validated way to build the command and a -params-file. See references/running-pipelines.md.
Write a minimal pipeline
#!/usr/bin/env nextflow
process SAYHELLO {
tag "$greeting"
publishDir "results", mode: 'copy'
input:
val greeting
output:
path "${greeting}.txt"
script:
"""
echo '$greeting world' > ${greeting}.txt
"""
}
workflow {
channel.of('hello', 'bonjour', 'hola') | SAYHELLO
}
nextflow run main.nf # add -resume on reruns
The full language (processes, channels, operators, DSL2 workflows with take/main/emit, modules) is in references/language.md.
Core Concepts at a Glance
- Process: a unit of work that runs a script (Bash by default). Declares
input:,output:, optionaldirectives(resources, container,publishDir,tag,errorStrategy), and ascript:/shell:/exec:block. Each task runs in its own isolated work directory (work/xx/yy…). - Channel: the async queues that connect processes. Queue channels are consumable streams; value channels hold a single reusable value. Created with factories like
channel.of,channel.fromPath,channel.fromFilePairs,channel.value. - Operator: transforms/combines channels —
map,filter,collect,groupTuple,join,combine,mix,flatten,branch,multiMap,splitCsv,view,set. - Workflow: composes processes. DSL2 workflows can declare
take:(inputs),main:(logic),emit:(named outputs) and beincluded as subworkflows. The unnamedworkflow {}is the entry point. - Module: a
.nffile exposing processes/workflows viainclude { NAME } from './path'(supportsasaliasing). - Configuration:
nextflow.configsetsparams,processdirectives,executor, container engines, and namedprofiles. SelectorswithName:/withLabel:target specific processes. Seereferences/configuration.md. - meta map (nf-core): the convention of carrying a metadata map (
[ id:'sample1', single_end:false ]) alongside files in input/output tuples so samples stay labeled through the pipeline. Seereferences/developing.md.
nf-core tools CLI
nf-core tools (v3+) group subcommands under pipelines, modules, and subworkflows. (Bare forms like nf-core lint still work but warn — prefer the grouped form.)
| Command | Purpose |
|---|---|
nf-core pipelines list | List/search nf-core pipelines (--json, keywords) |
nf-core pipelines create | Scaffold a new pipeline from the nf-core template |
nf-core pipelines launch <name> | Interactive, schema-driven run command + params file |
nf-core pipelines download <name> | Download pipeline + containers for offline/HPC use |
nf-core pipelines lint | Lint a pipeline against nf-core standards (run in repo root) |
nf-core pipelines schema build | Build/edit nextflow_schema.json via web GUI |
nf-core pipelines create-params-file <name> | Generate a documented YAML params file |
nf-core pipelines bump-version / sync | Bump version / sync with template updates |
nf-core modules list/info/install/update/remove | Manage modules from nf-core/modules |
nf-core modules create / lint / test | Author, lint, and nf-test a module |
nf-core modules patch / bump-versions | Patch an installed module / bump tool versions |
nf-core subworkflows install/create/lint/test | Same lifecycle for subworkflows |
Full command reference, flags, and examples: references/nf-core-tools.md.
Essential nextflow CLI
| Command | Purpose |
|---|---|
nextflow run <pipeline> -profile <p> --outdir <dir> | Run a pipeline (path, .nf, or user/repo) |
-resume | Reuse cached results from prior run |
-r <rev> | Run a specific git revision/tag/branch |
-params-file params.yml | Supply parameters from YAML/JSON |
-c custom.config | Layer in an extra config file |
-with-report -with-trace -with-timeline -with-dag flow.html | Execution report, trace, timeline, DAG |
-stub-run | Run stub: blocks only (dry-run plumbing) |
nextflow log | Inspect past runs |
nextflow clean -f -before <run> | Delete old work/ data |
nextflow pull / drop / list / info <repo> | Manage cached remote pipelines |
Config, executors, caching internals, and tracing details: references/configuration.md.
Best Practices (high-value habits)
- Always
testfirst:-profile test,docker(orsingularity/conda) before real data — fast and catches environment problems. - Pin everything: pipeline revision (
-r),NXF_VER, and tool versions (containers). Don't runlatestfor science you'll publish. - Use
-resumeand understand caching: a task re-runs if its inputs, script, or container change. See cache-debugging inreferences/configuration.md. - Parameterize via config/params-file, not hardcoded paths. Keep
paramsand profiles innextflow.config. - One container/conda env per process; never rely on tools installed on the host.
- For nf-core dev: reuse existing modules (
nf-core modules install) before writing new ones; pass tool flags throughext.args(not hardcoded in the script); always include astub:block and nf-test tests; runnf-core pipelines lintandprettierbefore committing. - Right-size resources with
process_low/medium/highlabels anderrorStrategy 'retry'with dynamictask.attemptscaling instead of one giant request. - Write forward-compatible syntax: the strict-syntax parser becomes the default in Nextflow 26.04. Prefer lowercase
channel.of(...), explicit closure params ({ v -> ... }),deffor all variables, andemit:-named outputs. Check withnextflow lint.
Reference Files
Read the relevant file when you need depth — each is self-contained:
references/language.md— DSL2 language: processes, directives, channels, operators, workflows (take/emit), modules, dynamic resources, error handling.references/configuration.md—nextflow.config, scopes,profiles,withName/withLabelselectors, executors (local/SLURM/cloud), caching/-resumeinternals, tracing/reports, thenextflowCLI.references/containers.md— Docker, Singularity/Apptainer, Podman, Conda, Wave containers; choosing and enabling engines; common gotchas.references/running-pipelines.md— finding/running nf-core pipelines, samplesheets, params files, reference genomes (iGenomes), offline runs, institutional configs, Seqera Platform.references/nf-core-tools.md— completenf-coreCLI reference (pipelines/modules/subworkflows), flags, and workflows.references/developing.md— authoring nf-core pipelines & modules: template layout, modulemain.nf/meta.yml, meta maps,ext.args/modules.config, subworkflows, resource labels, linting & Harshil alignment style.references/testing.md— nf-test for modules/subworkflows/pipelines: test structure, assertions, snapshots, tags, running tests, CI.
Official docs: Nextflow https://www.nextflow.io/docs/latest/ · nf-core https://nf-co.re/docs/ · Training https://training.nextflow.io/
Frequently asked questions about Nextflow
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