### Nextflow
Works with
name: "nextflow"
description: "Build, run, and debug Nextflow data pipelines and nf-core workflows end to end. Use whenever the user mentions Nextflow, nf-core, .nf files, nextflow.config, DSL2, processes/channels/operators, sample..."
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionnextflowExecute the skills CLI command in your project's root directory to begin installation:
Fetches nextflow from K-Dense-AI/scientific-agent-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate nextflow. Access via /nextflow in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
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| name | nextflow |
| description | Build, run, and debug Nextflow data pipelines and nf-core workflows end to end. Use whenever the user mentions Nextflow, nf-core, .nf files, nextflow.config, DSL2, processes/channels/operators, samplesheets, or wants to run a community pipeline (e.g. nf-core/rnaseq, nf-core/sarek), write or test a module/subworkflow with nf-test, configure executors/containers (Docker, Singularity/Apptainer, Conda, Wave), scale a workflow to HPC/SLURM or cloud (AWS Batch, Google Batch, Azure, Kubernetes), or debug a failed/-resume run. Make sure to use this skill for any reproducible scientific/bioinformatics workflow work even if the user does not say the word "Nextflow", and for authoring nf-core-compliant pipelines, modules, configs, and linting. |
| license | Apache-2.0 |
| metadata | version: "1.0" skill-author: K-Dense Inc. |
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:
process tasks connected by channels. Nextflow infers execution order and parallelism from data dependencies — there is no explicit scheduler to write.-resume caching + pinned pipeline revisions.process/workflow/include definitions.This skill covers both running existing pipelines and developing your own (Nextflow language + nf-core conventions, testing with nf-test, configuration, and deployment).
Use this skill when the user wants to:
.nf scripts, nextflow.config, profiles, or nextflow_schema.json.main.nf, meta.yml, tests/, nf-test).take/emit, publishDir, ext.args, meta maps.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
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.
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 |
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.-resume reuses 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.
#!/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.
input:, output:, optional directives (resources, container, publishDir, tag, errorStrategy), and a script:/shell:/exec: block. Each task runs in its own isolated work directory (work/xx/yy…).channel.of, channel.fromPath, channel.fromFilePairs, channel.value.map, filter, collect, groupTuple, join, combine, mix, flatten, branch, multiMap, splitCsv, view, set.take: (inputs), main: (logic), emit: (named outputs) and be included as subworkflows. The unnamed workflow {} is the entry point..nf file exposing processes/workflows via include { NAME } from './path' (supports as aliasing).nextflow.config sets params, process directives, executor, container engines, and named profiles. Selectors withName:/withLabel: target specific processes. See references/configuration.md.[ id:'sample1', single_end:false ]) alongside files in input/output tuples so samples stay labeled through the pipeline. See references/developing.md.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.
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.
test first: -profile test,docker (or singularity/conda) before real data — fast and catches environment problems.-r), NXF_VER, and tool versions (containers). Don't run latest for science you'll publish.-resume and understand caching: a task re-runs if its inputs, script, or container change. See cache-debugging in references/configuration.md.params and profiles in nextflow.config.nf-core modules install) before writing new ones; pass tool flags through ext.args (not hardcoded in the script); always include a stub: block and nf-test tests; run nf-core pipelines lint and prettier before committing.process_low/medium/high labels and errorStrategy 'retry' with dynamic task.attempt scaling instead of one giant request.channel.of(...), explicit closure params ({ v -> ... }), def for all variables, and emit:-named outputs. Check with nextflow lint.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/withLabel selectors, executors (local/SLURM/cloud), caching/-resume internals, tracing/reports, the nextflow CLI.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 — complete nf-core CLI reference (pipelines/modules/subworkflows), flags, and workflows.references/developing.md — authoring nf-core pipelines & modules: template layout, module main.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/
Prerequisites
Time Estimate
15-45 minutes depending on use case complexity
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.
✗ Avoid when
Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.
K-Dense-AI/scientific-agent-skills
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google-deepmind/science-skills
google-deepmind/science-skills
Useful defaults in nextflow — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
Keeps context tight: nextflow is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend nextflow for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
nextflow has been reliable in day-to-day use. Documentation quality is above average for community skills.
nextflow fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Solid pick for teams standardizing on skills: nextflow is focused, and the summary matches what you get after install.
nextflow fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
Keeps context tight: nextflow is the kind of skill you can hand to a new teammate without a long onboarding doc.
I recommend nextflow for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: nextflow is focused, and the summary matches what you get after install.
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