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.
What this skill does
# 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:
| 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.
```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 `include`d 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.)
| 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 |
| `-pRelated in Cloud & DevOps
appbuilder-action-scaffolder
IncludedCreate, implement, deploy, and debug Adobe Runtime actions with consistent layout, validation, and error handling. Use this skill whenever the user needs to add actions to an App Builder project, understand action structure (params, response format, web/raw actions), configure actions in the manifest, use App Builder SDKs (State, Files, Events, database), deploy and invoke actions via CLI, debug action issues, or implement patterns such as webhook receivers, custom event providers, journaling consumers, large payload redirects, action sequence pipelines, and Asset Compute workers. Also trigger when users mention serverless functions in Adobe context, action logging, IMS authentication for actions, or cron-style scheduled actions.
orchestrating-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. Use this skill when the user needs a multi-step Data Cloud pipeline, cross-phase troubleshooting, or data space and data kit management. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase sf data360 workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching phase-specific skill), the task is STDM/session tracing/parquet telemetry (use observing-agentforce), standard CRM SOQL (use querying-soql), or Apex implementation (use generating-apex).
github-project-automation
IncludedAutomate GitHub repository setup with CI/CD workflows, issue templates, Dependabot, and CodeQL security scanning. Includes 12 production-tested workflows and prevents 18 errors: YAML syntax, action pinning, and configuration. Use when: setting up GitHub Actions CI/CD, creating issue/PR templates, enabling Dependabot or CodeQL scanning, deploying to Cloudflare Workers, implementing matrix testing, or troubleshooting YAML indentation, action version pinning, secrets syntax, runner versions, or CodeQL configuration. Keywords: github actions, github workflow, ci/cd, issue templates, pull request templates, dependabot, codeql, security scanning, yaml syntax, github automation, repository setup, workflow templates, github actions matrix, secrets management, branch protection, codeowners, github projects, continuous integration, continuous deployment, workflow syntax error, action version pinning, runner version, github context, yaml indentation error
sf-datacloud
IncludedSalesforce Data Cloud product orchestrator for connect→prepare→harmonize→segment→act workflows. TRIGGER when: user needs a multi-step Data Cloud pipeline, asks to set up or troubleshoot Data Cloud across phases, manages data spaces or data kits, or wants a cross-phase `sf data360` workflow. DO NOT TRIGGER when: work is isolated to a single phase (use the matching sf-datacloud-* skill), the task is STDM/session tracing/parquet telemetry (use sf-ai-agentforce-observability), standard CRM SOQL (use sf-soql), or Apex implementation (use sf-apex).
fabric-cli
IncludedUse this skill for Fabric.so CLI workflows with the `fabric` terminal command: diagnose/install/login, search or browse a Fabric library, save notes/links/files, create folders, ask the Fabric AI assistant, manage tasks/workspaces, generate shell completion, check subscription usage, produce JSON output, and use Fabric as persistent agent memory. Do not use for Microsoft Fabric/Azure/Power BI `fab`, Daniel Miessler's Fabric framework, Python Fabric SSH, Fabric.js, or textile/fashion fabric.
lark
IncludedLark/Feishu CLI skills: lark-cli operations for docs, markdown, sheets, base, calendar, im, mail, task, okr, drive, wiki, slides, whiteboard, apps, approval, attendance, contact, vc, minutes, event. Use when the user needs to operate Lark/Feishu resources via lark-cli, send messages, manage documents, spreadsheets, calendars, tasks, OKRs, deploy web pages, or any Feishu/Lark workspace operations.