Skills Agentes

Nextflow

Construye, ejecuta y depura pipelines Nextflow y workflows nf-core de extremo a extremo: módulos DSL2, samplesheets, contenedores, ejecutores HPC/nube y runs con -resume, incluyendo pipelines como nf-core/rnaseq o nf-core/sarek.

Estrellas
45.9k

en todo el repo

Actividad
60

0–100, la ruta de este skill

Actualizado
hace 2 meses

último commit aquí

Commits
4

últimos 90 días

Contexto
3.1k tok

186 tok en reposo

Paquete
8 archivos

77 KB

Instalar

Funciona con cualquier agente que lea SKILL.md

npx -y skills add K-Dense-AI/scientific-agent-skills --skill nextflow --agent claude-code

Se instala solo en este repositorio.

Este skill makes network requests.

Qué hace

  • Ejecuta pipelines nf-core existentes o personalizados y depura runs fallidos o que usan -resume
  • Escribe y modifica scripts .nf, nextflow.config, profiles y nextflow_schema.json
  • Autoría o testea módulos/subworkflows al estilo nf-core (main.nf, meta.yml, tests con nf-test)
  • Configura ejecutores, contenedores (Docker, Singularity/Apptainer, Conda, Wave) y escalado a HPC o nube
  • Explica procesos, canales, operadores, take/emit, publishDir, ext.args y meta maps del lenguaje Nextflow

Úsalo cuando

  • Se quiere ejecutar un pipeline nf-core o Nextflow personalizado, o depurar un run que falla o se resume
  • Se escriben o modifican archivos .nf, nextflow.config, profiles o nextflow_schema.json
  • Se autorean o testean módulos/subworkflows estilo nf-core con nf-test
  • Se configuran ejecutores/contenedores o se escala el workflow a HPC o nube

No lo uses cuando

    Qué lo activa

    Di cualquiera de estas frases y el agente debería cargar este skill.

    • “Ejecuta nf-core/rnaseq con Docker sobre este samplesheet”
    • “Escribe un módulo nf-core con su archivo meta.yml y tests nf-test”
    • “Configura este pipeline para correr en SLURM con Singularity”

    SKILL.md

    En inglés

    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.

    # 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
    
    # nf-core tools (Python) for creating/linting/running nf-core assets
    uv 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.

    # 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

    #!/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
    }
    
    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.)

    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
    -params-file params.yml Supply parameters from YAML/JSON
    -c custom.config Layer in an extra config file
    -with-report -with-trace -with-timeline -with-dag flow.html Execution report, trace, timeline, DAG
    -stub-run Run stub: blocks only (dry-run plumbing)
    nextflow log Inspect 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/

    Reproducido de K-Dense-AI/scientific-agent-skills bajo licencia Apache-2.0. Leer esta página en markdown.

    Archivos

    8 archivos en el paquete. Solo se lee SKILL.md al activarse — las referencias se cargan si el skill decide que las necesita.

    Antes de instalar

    Necesita Bash y Java 17 o superior; se instala con `curl -s https://get.nextflow.io | bash` o vía conda, y las nf-core tools con `uv pip install nf-core`.

    Necesita en el PATH:curl

    Detalles

    Creador
    K-Dense-AI
    Categoría
    Automatización
    Licencia
    Apache-2.0
    Recursos incluidos
    referencias
    Código fuente
    Ver SKILL.md

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