nextflow

K-Dense-AI/scientific-agent-skills · updated Jun 4, 2026

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$npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill nextflow
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### Nextflow

  • 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..."
skill.md
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

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 process tasks 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) + -resume caching + pinned pipeline revisions.
  • DSL2 is the modern, required syntax: modular 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).

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 .nf scripts, nextflow.config, profiles, or nextflow_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
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:

GoalStart here
Run an existing pipeline (nf-core or a .nf you were given)references/running-pipelines.md
Develop a new pipeline / module / subworkflowreferences/language.md + references/developing.md
Configure / scale (HPC, cloud, containers, resources)references/configuration.md + references/containers.md
Test modules/pipelinesreferences/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.
  • -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.

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:, 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: 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 be included as subworkflows. The unnamed workflow {} is the entry point.
  • Module: a .nf file exposing processes/workflows via include { NAME } from './path' (supports as aliasing).
  • Configuration: nextflow.config sets params, process directives, executor, container engines, and named profiles. Selectors withName:/withLabel: target specific processes. See references/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. See references/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.)

CommandPurpose
nf-core pipelines listList/search nf-core pipelines (--json, keywords)
nf-core pipelines createScaffold 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 lintLint a pipeline against nf-core standards (run in repo root)
nf-core pipelines schema buildBuild/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 / syncBump version / sync with template updates
nf-core modules list/info/install/update/removeManage modules from nf-core/modules
nf-core modules create / lint / testAuthor, lint, and nf-test a module
nf-core modules patch / bump-versionsPatch an installed module / bump tool versions
nf-core subworkflows install/create/lint/testSame lifecycle for subworkflows

Full command reference, flags, and examples: references/nf-core-tools.md.

Essential nextflow CLI

CommandPurpose
nextflow run <pipeline> -profile <p> --outdir <dir>Run a pipeline (path, .nf, or user/repo)
-resumeReuse cached results from prior run
-r <rev>Run a specific git revision/tag/branch
-params-file params.ymlSupply parameters from YAML/JSON
-c custom.configLayer in an extra config file
-with-report -with-trace -with-timeline -with-dag flow.htmlExecution report, trace, timeline, DAG
-stub-runRun stub: blocks only (dry-run plumbing)
nextflow logInspect 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 test first: -profile test,docker (or singularity/conda) before real data — fast and catches environment problems.
  • Pin everything: pipeline revision (-r), NXF_VER, and tool versions (containers). Don't run latest for science you'll publish.
  • Use -resume and understand caching: a task re-runs if its inputs, script, or container change. See cache-debugging in references/configuration.md.
  • Parameterize via config/params-file, not hardcoded paths. Keep params and profiles in nextflow.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 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.
  • Right-size resources with process_low/medium/high labels and errorStrategy 'retry' with dynamic task.attempt scaling 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 -> ... }), def for all variables, and emit:-named outputs. Check with nextflow 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.mdnextflow.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/

how to use nextflow

How to use nextflow on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add nextflow
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill nextflow

The skills CLI fetches nextflow from GitHub repository K-Dense-AI/scientific-agent-skills and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/nextflow

Reload or restart Cursor to activate nextflow. Access the skill through slash commands (e.g., /nextflow) or your agent's skill management interface.

Security & Verification Notice

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.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Installation Steps

  1. 1.Install skill using provided installation command
  2. 2.Test with simple use case relevant to your work
  3. 3.Evaluate output quality and relevance
  4. 4.Iterate on prompts to improve results
  5. 5.Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ 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.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.
general reviews

Ratings

4.438 reviews
  • Mia Srinivasan· Dec 24, 2024

    Useful defaults in nextflow — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Noor Haddad· Dec 16, 2024

    Keeps context tight: nextflow is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Min Okafor· Nov 15, 2024

    I recommend nextflow for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Dev Dixit· Nov 7, 2024

    nextflow has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Arya Verma· Oct 26, 2024

    nextflow fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Mateo Tandon· Oct 6, 2024

    Solid pick for teams standardizing on skills: nextflow is focused, and the summary matches what you get after install.

  • Oshnikdeep· Sep 21, 2024

    nextflow fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Fatima Ramirez· Sep 13, 2024

    Keeps context tight: nextflow is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Mateo Thomas· Sep 5, 2024

    I recommend nextflow for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Sofia Liu· Aug 24, 2024

    Solid pick for teams standardizing on skills: nextflow is focused, and the summary matches what you get after install.

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