typed-nextflow

name	typed-nextflow
description	Use when writing or modifying Nextflow (.nf) scripts on Nextflow 25.10+ / 26.04+, especially when output may default to legacy untyped DSL2 patterns (tuple inputs, `publishDir`, `Channel.from`, `|` pipes, implicit `it`, `set`/`tap`, `splitCsv` as operator). Reference for strict syntax, typed processes, typed workflows, records, typed params, workflow output blocks, and typed operator equivalents.
metadata	{"type":"reference"}

Writing Typed Nextflow (26.04+)

Why this skill exists

Most Nextflow code in pretraining data is legacy untyped DSL2: tuple val(id), path(reads) inputs, publishDir, Channel.from, implicit it, set/tap, | pipes, splitCsv as a channel operator. As of Nextflow 25.10 / 26.04 these patterns are deprecated or unsupported under strict syntax + static types. Without explicit prompting, models regress to the old style. Use this skill whenever generating or editing .nf files unless the project explicitly targets an older Nextflow.

Quick decision

• New code, no version constraint stated → emit typed strict-syntax (26.04 style).
• Editing a legacy or partially-typed file → first state the typed equivalent at the top of your response in one or two lines ("modernized, this would be: tuple(id: String, …) and output {} instead of publishDir"), then apply the edit in the file's existing style unless the user opts into a rewrite. This surfaces drift without forcing churn.
• Project pins Nextflow < 25.10 → use legacy DSL2 (but still avoid deprecated patterns: Channel.from, implicit it, set/tap, multi-arg mix).

STOP — most common regressions in typed code

Even when emitting "typed" Nextflow, models routinely get these wrong. Check each before submitting:

1. Process input destructuring (26.04 breaking change).

   • 25.10: (id, fastq_1, fastq_2): Tuple<String, Path, Path>
   • 26.04: tuple(id: String, fastq_1: Path, fastq_2: Path) The old (name1, name2): Tuple<A, B> syntax in an input: block is rejected in 26.04. Tuple<A, B> is still valid as a channel element type (e.g., Channel<Tuple<String, Path>>), just not as the process input destructure form.

2. groupBy does not take a key closure. It expects the channel to already be (key, value) 2-tuples (or (key, size, value) 3-tuples). To group by a computed key, map first:

   // wrong
   pairs.groupBy { p -> p[0] }
   // right
   pairs.map { id, file -> tuple(id, file) }.groupBy()
   // → emits (id, Bag<files>)
   

3. Pass grouped data as one tuple input, not two parallel channels. A grouped channel of (id, [files]) must go into one process input, not split into two .map derivations passed positionally — that breaks synchronization. Destructure inside the process:

   process MERGE_FILES {
       input:
       tuple(id: String, files: List<Path>)
       // ...
   }
   workflow {
       grouped = pairs.groupBy()      // Channel<Tuple<String, Bag<Path>>>
       MERGE_FILES(grouped)            // single channel argument
   }
   

4. Process section order matters. Required order: input: → stage: (optional) → output: → topic: (optional) → script:/exec: → stub: (optional). Putting output: after script: is a parse error in strict syntax.

5. Outputs are values, not bare identifiers. Inside output: each line is either an unnamed value (stdout(), file('x.txt'), record(...)) or name: Type = value. The most common regression when migrating a legacy process is dropping the input/output qualifier and leaving a bare variable name:

Enabling the new mode

Every typed script needs both:

nextflow.enable.types = true

// (strict syntax is on by default in 26.04; for 25.10 export NXF_SYNTAX_PARSER=v2)

The params {} block and the output {} block work without nextflow.enable.types. Typed processes and typed workflows (take:/emit: annotations) require the flag.

Core building blocks

1. params {} block with types

Replace top-level params.foo = ... with a typed block:

params {
    // Samplesheet path
    reads: Path = "${projectDir}/data/samples.csv"

    // Optional reference; required if no default given
    transcriptome: Path

    // Default-false boolean
    save_intermeds: Boolean

    outdir: Path = 'results'
}

Rules:

• params are only meant for the entry workflow and the output {} block — pass them as explicit inputs to subworkflows/processes.
• A parameter without a default is required (run fails if unset). Booleans without defaults default to false in 26.04.
• CLI values are coerced to the declared type.

2. Records replace tuples

A record is a named-field composite. Construct with record(...) and define a type with record Name { ... }:

record Sample {
    id: String
    fastq_1: Path
    fastq_2: Path?     // `?` = nullable
}

def s = record(id: 'A', fastq_1: file('a_1.fq'), fastq_2: file('a_2.fq'))
s.id      // access by name, not index

Records are duck-typed: a record satisfies an input as long as it contains all the fields the input declares. Extra fields are ignored.

2b. nf-core meta map → record migration

The dominant pattern in pretraining data is the nf-core (meta, files) tuple, where meta is a Groovy map:

// legacy nf-core
process FASTQC {
    tag "${meta.id}"
    input:
    tuple val(meta), path(reads)
    output:
    tuple val(meta), path('*_fastqc.zip'), emit: zip
    // ...
}
workflow {
    ch_reads = Channel
        .fromPath(params.samplesheet)
        .splitCsv(header: true)
        .map { row ->
            def meta = [id: row.sample, single_end: row.single_end.toBoolean()]
            def reads = meta.single_end ? [file(row.fastq_1)] : [file(row.fastq_1), file(row.fastq_2)]
            tuple(meta, reads)
        }
    FASTQC(ch_reads)
}

Typed equivalent — flatten the meta into the record, do not nest it:

nextflow.enable.types = true

record Sample {
    id: String
    single_end: Boolean
    strandedness: String?
    reads: List<Path>
}

process FASTQC {
    tag sample.id
    input:
    sample: Sample
    output:
    record(
        id: sample.id,
        single_end: sample.single_end,
        zip: files('*_fastqc.zip'),
        html: files('*_fastqc.html')
    )
    script:
    """
    fastqc --threads ${task.cpus} ${sample.reads.join(' ')}
    """
}

workflow {
    main:
    samples = channel.of(params.samplesheet)
        .flatMap { csv -> csv.splitCsv(header: true) }
        .map { row ->
            def single = row.single_end.toBoolean()
            record(
                id: row.sample,
                single_end: single,
                strandedness: row.strandedness ?: null,
                reads: single
                    ? [file(row.fastq_1, checkIfExists: true)]
                    : [file(row.fastq_1, checkIfExists: true), file(row.fastq_2, checkIfExists: true)]
            )
        }

    fastqc = FASTQC(samples)

    publish:
    fastqc = fastqc
}

output {
    fastqc: Channel<Record> {
        // per-sample subdir, by `id` directly (no `meta.id`)
        path { r -> "fastqc/${r.id}" }
        index { path 'fastqc.csv'; header true }
    }
}

Conventions when migrating from nf-core meta:

• meta + [type: 'long'] map-merge → s + record(type: 'long') record-merge.
• meta.id access → s.id directly; tag "${meta.id}" → tag sample.id.
• meta.single_end ? [r1] : [r1, r2] stays the same shape, but reads become a typed List<Path> field on the record.
• Per-process publishing via publishDir { "results/fastqc/${meta.id}" } → output { fastqc { path { r -> "fastqc/${r.id}" } } }.
• nf-core's task.ext.args / task.ext.prefix still work in typed processes; they aren't deprecated. Only meta-map conventions need rewriting.

3. Typed processes

nextflow.enable.types = true

process FASTQC {
    tag sample.id
    conda 'bioconda::fastqc=0.12.1'

    input:
    sample: Sample                  // by record type
    // or destructured:
    // record(id: String, fastq_1: Path, fastq_2: Path)
    // or tuple destructure:
    // tuple(id: String, fastq: Path)
    // or scalar:
    // index: Path

    output:
    record(
        id: sample.id,
        fastqc: file("fastqc_${sample.id}_logs")
    )

    script:
    """
    fastqc.sh ${sample.id} ${sample.fastq_1} ${sample.fastq_2}
    """
}

Key points:

• Each input is name: Type (or a destructured record(...) / tuple(...)).
• Type replaces qualifier: path → Path, val → the actual type, path '*' collection → Set<Path> (or List<Path> if order matters).
• Path inputs and Path-collections are auto-staged. Default stage pattern for collections is '*'.
• Nullable inputs: input: Path? (otherwise null fails the task).
• Outputs are regular values built with file(), files(), stdout(), env('VAR'), record(...), tuple(...). Single unnamed output is allowed.
• file('x', optional: true) returns null if missing instead of failing.
• each input qualifier is gone — use the combine operator at the call site.

4. The stage: section (replaces leftover qualifiers)

Custom staging that used to ride on input qualifiers now lives in stage::

process grep {
    input:
    id: String
    fasta: Path

    stage:
    stageAs fasta, "${id}.fa"      // note: (value, pattern) order in 26.04
    env 'SAMPLE_ID', id
    stdin id

    script:
    "cat ${id}.fa | grep '>'"
}

Method signature for stageAs changed in 26.04 to (value, pattern) — value first.

5. Typed workflows

nextflow.enable.types = true

workflow RNASEQ {
    take:
    samples: Channel<Sample>
    transcriptome: Path

    main:
    index   = INDEX(transcriptome)
    fastqc  = FASTQC(samples)
    quant   = QUANT(samples, index)
    joined  = fastqc.join(quant, by: 'id')

    emit:
    samples: Channel<AlignedSample> = joined
}

record AlignedSample {
    id: String
    fastqc: Path
    quant: Path
}

• Channel<T> for streaming, Value<T> (or T shorthand) for a singleton dataflow value.
• Emit annotations are optional but recommended as docs + sanity check.
• Restricted in typed workflows: no Channel. capital, no implicit it, no set/tap, no |/&, no .out. Use plain assignments and method calls.

6. Workflow outputs (replaces publishDir)

Define what gets published once, at the top level. Drop publishDir from processes.

workflow {
    main:
    samples_ch = channel.of(params.reads)
        .flatMap { csv -> csv.splitCsv(header: true) }
        .map { row -> record(id: row.id, fastq_1: file(row.fastq_1), fastq_2: file(row.fastq_2)) }

    aligned_ch = RNASEQ(samples_ch, params.transcriptome)
    report     = MULTIQC(aligned_ch.flatMap { s -> [s.fastqc, s.quant] }.collect(), params.multiqc)

    publish:
    samples         = aligned_ch
    multiqc_report  = report
}

output {
    samples: Channel<AlignedSample> {
        path { s ->
            s.fastqc >> "fastqc/${s.id}"
            s.quant  >> "quant/${s.id}"
        }
        index {
            path 'samples.csv'
            header true
        }
    }

    multiqc_report: Path {
        path 'multiqc_report.html'
    }
}

Config side:

workflow.output.mode = 'copy'
outputDir = 'results'      // or use -output-dir on CLI

manifest.defaultBranch is deprecated in 26.04 — drop it.

Strict syntax cheatsheet (what's banned/required)

Banned / deprecated	Use instead
import groovy.json.JsonSlurper	new groovy.json.JsonSlurper() (fully qualified)
class Foo { ... } in script	enum, record, or put in lib/
top-level statements mixed with declarations	put statements inside workflow {}
for/while loops	.each {}, .collect {}, .find {}, etc.
switch	if/else if/else
spread *list	enumerate or destructure
${PWD} implicit env	env('PWD')
addParams/params on include	pass as explicit inputs
def Map x = [:] Groovy-typed var	def x: Map = [:] (Nextflow-typed) or untyped def x = [:]
slashy strings with ${...} or multi-line	double-quoted / triple-quoted
(Map) x soft cast	x as Map
env FOO unquoted	env 'FOO'
process.shell section	script:
Channel.of(...)	channel.of(...)
implicit it	{ v -> ... }
workflow.onComplete { ... } at top level	put inside entry workflow as onComplete: section
process when: section	filter at the call site
inside non-entry workflows

Operator migration map (under nextflow.enable.types)

Prefer core operators; rewrite legacy ones.

Legacy	Replacement
set { ch }	ch = …
tap { ch }	assign before/after the next op
branch { ... }	one filter/map per branch (records make this easy)
multiMap { ... }	one map per branch, or single record with all fields
groupTuple	groupBy — input must already be (key, value) 2-tuples; no key closure. To group by a derived key, .map { x -> tuple(key(x), x) }.groupBy(). Emits (key, Bag<values>). See callout #2 above.
cross	join(other, by: 'id') on records
combine(by: 0)	join(other, by: 'id')
combine(other)	combine(other) (still core); for adding constant fields: ch.combine(field: 'auto', ref: x)
concat	mix (one arg per call: a.mix(b).mix(c))
mix(b, c) (multi-arg)	a.mix(b).mix(c)
merge	join (deterministic)
flatten	flatMap { … -> [...] }
transpose	flatMap { k, vs -> vs.collect { v -> tuple(k, v) } }
splitCsv / splitFasta / splitFastq / splitJson / splitText as operator	flatMap { f -> f.splitCsv(...) } (Path stdlib method)
toList	collect (collect now ≡ toList: not flattened, empty list on empty)

Tightened semantics with types:

• filter requires a closure (no bare regex literal).
• join requires by: (int for tuples, string for records). No failOnDuplicate/failOnMismatch for record joins — use remainder: and check.
• flatMap does not auto-flatten maps/tuples; the closure must return a collection.
• map does not drop null; chain .filter { v -> v != null }.
• mix takes a single argument per call.

Idiomatic skeleton

nextflow.enable.types = true

params {
    samplesheet: Path
    reference:   Path
    outdir:      Path = 'results'
}

record Sample {
    id: String
    fastq_1: Path
    fastq_2: Path?
}

include { ALIGN } from './modules/align'

workflow {
    main:
    samples = channel.of(params.samplesheet)
        .flatMap { csv -> csv.splitCsv(header: true) }
        .map { row ->
            record(
                id: row.id,
                fastq_1: file(row.fastq_1),
                fastq_2: row.fastq_2 ? file(row.fastq_2) : null
            )
        }

    aligned = ALIGN(samples, params.reference)

    publish:
    aligned = aligned
}

output {
    aligned: Channel<Record> {
        path { s -> "aligned/${s.id}" }
        index { path 'aligned.csv'; header true }
    }
}

Common mistakes (self-check before emitting code)

• Forgetting nextflow.enable.types = true while using typed input:/output:/take:/emit:.
• Writing tuple val(id), path(reads) — that's legacy. Use record(id: String, reads: Path) or tuple(id: String, reads: Path).
• Using path collection without specifying Set<Path> / List<Path>.
• Calling splitCsv directly on a channel — wrap in flatMap { f -> f.splitCsv(...) }.
• Using Channel.from(...) (capital + deprecated factory) → channel.of(...) or channel.fromList(...).
• Implicit it in closures — always declare { v -> ... }.
• MY_WF.out.foo — assign the call result first: out = MY_WF(); out.foo.
• Multi-arg mix(a, b, c) — chain .mix(a).mix(b).
• Emitting tuples where records would carry names through joins — switch to records to avoid branch/multiMap gymnastics.
• Leaving publishDir in processes alongside an output {} block.
• stageAs '*', value — argument order flipped in 26.04 to stageAs value, '*'.

Source docs

• Migrating to 26.04: https://nextflow.io/docs/latest/migrations/26-04.html
• Migrating to static types: https://nextflow.io/docs/latest/tutorials/static-types.html
• Operators under static types: https://nextflow.io/docs/latest/tutorials/static-types-operators.html
• Workflow outputs: https://nextflow.io/docs/latest/tutorials/workflow-outputs.html
• Typed processes: https://nextflow.io/docs/latest/process-typed.html
• Typed workflows: https://nextflow.io/docs/latest/workflow-typed.html
• Strict syntax: https://nextflow.io/docs/latest/strict-syntax.html

Tested against (subagent RED/GREEN, 2026-05-12)

Scenario	Result
Generic FastQC process from CSV samplesheet	clean on first pass
nf-core (meta, reads) tuple → typed	first pass nested meta inside the output record; callout #6 added → fixed
Edit a legacy partially-typed file (this repo's throughput/benchmark.nf), add new process	first pass used 25.10 destructure (name): Tuple<...> and bare-identifier output; callouts #1 and #5 added → fixed on retest
Group (id, file) pairs by id, call process once per id	first pass used groupBy { closure } and split into two parallel channels; callouts #2 and #3 added → fixed