Run Module

Run Nextflow Modules

Run modules from the Nextflow Registry natively using nextflow module commands. Everything is self-contained — no external tools or MCP needed. Modules are installed on-the-fly when run.

Modules are published under namespaces (e.g. nf-core/fastqc), and the nextflow module commands work with any of them.

Requires Nextflow 26.04 or later (for the nextflow module commands).

⛔ NEVER WRITE WRAPPER WORKFLOWS

If a module fails due to missing arguments or incorrect parameters:

❌ WRONG - Writing a wrapper workflow:

// NEVER DO THIS - even if the module fails!
include { FASTQC } from 'nf-core/fastqc'
workflow { FASTQC(Channel.fromPath('*.fq.gz')) }

✅ CORRECT - Use nextflow module view to get the correct parameters:

nextflow module view nf-core/fastqc

When a module run fails:

1. Run nextflow module view to get the command template
2. Fix the arguments based on the template
3. Re-run with nextflow module run ...

NEVER create a wrapper workflow as a "fix" for missing arguments.

Step 1: Search for the Module

Use nextflow module search with a natural language term — it performs similarity search on module name, description, and features:

nextflow module search "quality control"
nextflow module search "alignment"
nextflow module search "variant calling"
nextflow module search "BAM statistics"

Step 2: Get Module Info and Run Template

Once you've identified the module, get its detailed info and the command template:

nextflow module view nf-core/fastqc

This returns the module description, inputs, parameters, and the exact run command template. Use this template as the basis for your run command.

Step 3: Substitute Template Values and Run

Replace placeholder values in the template with the user's concrete values (file paths, parameters), then execute. No explicit install is needed — modules are fetched on-the-fly:

nextflow module run nf-core/fastqc \
  --input "/path/to/sample.fq.gz" \
  --outdir results

Container Provisioning with Wave + Conda

Modules require containers for their underlying tools. Configure nextflow.config to use Wave + Conda so containers are provisioned on-the-fly from each module's conda packages:

wave.enabled = true
wave.strategy = 'conda,container'
docker.enabled = true

This avoids the need to manually build or pull container images — Wave provisions them from the module's declared conda dependencies.

Commands Reference

| Command | Description | |---------|-------------| | nextflow module search | Similarity search by name/description/feature | | nextflow module view | Detailed info about the module and how to run it | | nextflow module run [options] | Run a module (installed on-the-fly) | | nextflow module list | List available modules |

Examples

Single-end FASTQ Quality Control

# 1. Search
nextflow module search "quality control"

# 2. Get the template
nextflow module view nf-core/fastqc

# 3. Run with concrete values
nextflow module run nf-core/fastqc \
  --input "data/sample.fq.gz" \
  --outdir results_fastqc

Paired-end Alignment

# 1. Search
nextflow module search "bwa alignment"

# 2. Get the template
nextflow module view nf-core/bwa/mem

# 3. Run with concrete values
nextflow module run nf-core/bwa/mem \
  --input "data/reads_1.fq.gz,data/reads_2.fq.gz" \
  --reference "data/genome.fa" \
  --outdir results_bwa

Complete Workflow: Search → Info → Run

1. Search for the module:

``bash nextflow module search "quality control for FASTQ" ``

1. Get info and run template:

``bash nextflow module view nf-core/fastqc ``

1. Substitute template placeholders with actual values from the user's data

1. Run the module:

``bash nextflow module run nf-core/fastqc \ --input "data/sample.fq.gz" \ --outdir results ``

1. Process output — Read the stdout, summarize key results, and suggest the logical next step to the user

Critical Rules

1. SEARCH FIRST — Always use nextflow module search to find the right module
2. GET THE TEMPLATE — Always run nextflow module view before running a module
3. SUBSTITUTE TEMPLATE VALUES — Replace all placeholders with concrete values from the user's data
4. NEVER write wrapper workflows — If a run fails, use nextflow module view to get correct args
5. NEVER guess parameters — Always get them from the info template
6. Expand wildcards first — Use ls data/*.fq then comma-separate results
7. Quote multi-file inputs — --input "file1,file2,file3"
8. Use absolute paths when possible
9. ALWAYS PROCESS STDOUT OUTPUT — After a successful run, present a summary and suggest the logical next step

When Module Run Fails

DO NOT write a wrapper workflow. Instead:

1. Run nextflow module view nf-core/
2. Compare the template with your command to find discrepancies
3. Fix your nextflow module run command with correct arguments
4. Re-run the corrected command

Step 4: Process Run Output (MANDATORY)

The nextflow module run command prints its output to stdout. After a module run, you MUST:

1. Read the stdout output from the run command
2. Present a clear summary of the results to the user — highlight key metrics, status, and any warnings or errors
3. Infer the next step — Based on the output and the module that was run, suggest what the user might want to do next. Examples:

• After fastqc: "Quality scores look good. Would you like to proceed to alignment with bwa/mem?"
• After bwa/mem: "Alignment complete. Would you like to sort the BAM with samtools/sort or get stats with samtools/stats?"
• After fastp: "Trimming done. Would you like to align the trimmed reads?"

Always frame next-step suggestions as questions to the user.

Only list or inspect output files in the work directory if the module or user specifically requires it — do not do this by default.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: nextflow-io
• Source: nextflow-io/agent-skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.