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Nextflow

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dralkh
nextflow

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.

Overview

Publisherdralkh
Repositoryseerai
Skill namenextflow
Stars
96
Forks
5
Bundled files
7
LicenseApache-2.0
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 7 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by dralkh on GitHub. Read the source before you install it.

Installation

Install the Nextflow AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/dralkh/seerai/tree/main/skills/nextflow
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/dralkh/seerai.git /tmp/seerai
mkdir -p .claude/skills
cp -r /tmp/seerai/skills/nextflow .claude/skills/nextflow
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Nextflow in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Nextflow on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Nextflow is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.

bash
# 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
bash
# 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.

bash
# 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

nextflow
#!/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
}
bash
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.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/

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Nextflow AI skill do?

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/bioinformati...

Why use Nextflow on TypingMind?

Because you install it once and use it with any model. Nextflow is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Nextflow in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dralkh/seerai/tree/main/skills/nextflow. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Nextflow?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Nextflow?

As many as you like. As long as a model supports skills, you can use Nextflow with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Nextflow AI skill free?

Yes. It is published on GitHub by dralkh under the Apache-2.0 license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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