From nextflow
Creates Nextflow workflows by composing validated modules from the Nextflow Registry. Guides users through module search, plan approval, and sequential module validation with test data.
How this skill is triggered — by the user, by Claude, or both
Slash command
/nextflow:create-workflowThis skill is limited to the following tools:
The summary Claude sees in its skill listing — used to decide when to auto-load this skill
Create complete Nextflow workflows by composing validated modules from the [Nextflow Registry](https://registry.nextflow.io).
Create complete Nextflow workflows by composing validated modules from the Nextflow Registry.
Modules are published under namespaces (e.g. nf-core/fastqc), and these skills compose modules from any of them.
Requires Nextflow 26.04 or later (for the nextflow module commands used during validation).
NEVER write a wrapper workflow just to run/test a single module.
❌ WRONG - Writing a workflow to run one module:
// DO NOT DO THIS - not even when a module run fails!
include { FASTQC } from 'nf-core/fastqc'
workflow { FASTQC(Channel.fromPath('data/*.fq.gz')) }
✅ CORRECT - Use the run-module skill:
Skill(skill="run-module")
The run-module skill:
nextflow module search/view to discover and get proper inputs/parametersnextflow module run <namespace>/<module>⚠️ When a module run fails due to missing args:
nextflow module view <module> to get correct parametersOnly write a workflow in Step 4 when composing multiple validated modules together.
ALWAYS follow this structured process when creating a new workflow:
nextflow module search <term> to find Registry modules for each processing stepnextflow module view <name> to understand inputs/outputs of each moduleSTOP and wait for user approval before proceeding.
NEVER skip this step. After user agreement:
Skill(skill="run-module").modules-validation- prefixNote: The
run-moduleskill usesnextflow modulecommands for discovery, configuration, and execution — modules are installed on-the-fly.
DO NOT proceed to Step 4 until ALL modules have been individually validated.
Only after ALL modules run successfully:
Configure nextflow.config with Wave + Conda:
wave.enabled = true
wave.strategy = 'conda,container'
docker.enabled = true
Write the workflow script compositing all validated steps using Nextflow managed modules (no ./ prefix — Nextflow automatically downloads and installs them from the Nextflow Registry):
include { MODULE_A } from 'nf-core/module_a'
include { MODULE_B } from 'nf-core/module_b'
workflow {
MODULE_A(input_ch)
MODULE_B(MODULE_A.out.results)
}
Run the complete workflow using the same test data to validate end-to-end
-resume flag to leverage cached results when appropriate--input "file1.fq,file2.fq"include { MOD } from 'nf-core/module' — Nextflow managed module (default). Nextflow automatically downloads and installs it from the Nextflow Registry. Always prefer this form. (nf-core here is the namespace; substitute the actual namespace of the module you are using.)include { MOD } from './modules/nf-core/module/main.nf' — Local file path, resolved against the working directory. Only use when referencing locally modified modules.Skill(skill="run-module") insteadnextflow module search to find modules, then nextflow module view for details.command.log, .command.err, and .command.out filesDelegate module-specific tasks to specialized skills using the Skill tool:
| Task | Invoke Skill |
|---|---|
| Install/run/test a module | Skill(skill="run-module") |
run-module skill for each module validationFor each module in your plan:
1. Invoke: Skill(skill="run-module") → install, run with test data, and verify outputs
2. Only proceed to next module after success
These skills contain detailed instructions for their specific tasks and ensure consistent execution patterns.
Step 1: Identify modules → Propose plan to user
Step 2: Wait for user agreement
Step 3: Validate ALL modules ONE BY ONE with test data
Step 4: Compose final workflow (only after Step 3 succeeds)
npx claudepluginhub nextflow-io/agent-skills --plugin nextflowGuides creation, editing, and verification of skills for AI coding agents using test-driven development with subagent scenarios. Use when authoring or debugging skills.