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.
npx skills add https://github.com/K-Dense-AI/scientific-agent-skills --skill nextflow
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:
process tasks connected by channels. Nextflow infers execution order and parallelism from data dependencies — there is no explicit scheduler to write.-resume caching + pinned pipeline revisions.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).
Use this skill when the user wants to:
.nf scripts, nextflow.config, profiles, or nextflow_schema.json.main.nf, meta.yml, tests/, nf-test).take/emit, publishDir, ext.args, meta maps.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.
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 |
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.
#!/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.
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.of, channel.fromPath, channel.fromFilePairs, channel.value.map, filter, collect, groupTuple, join, combine, mix, flatten, branch, multiMap, splitCsv, view, set.take: (inputs), main: (logic), emit: (named outputs) and be included as subworkflows. The unnamed workflow {} is the entry point..nf file exposing processes/workflows via include { NAME } from './path' (supports as aliasing).nextflow.config sets params, process directives, executor, container engines, and named profiles. Selectors withName:/withLabel: target specific processes. See references/configuration.md.[ 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 (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.
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.
test first: -profile test,docker (or singularity/conda) before real data — fast and catches environment problems.-r), NXF_VER, and tool versions (containers). Don't run latest for science you'll publish.-resume and understand caching: a task re-runs if its inputs, script, or container change. See cache-debugging in references/configuration.md.params and profiles in nextflow.config.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.process_low/medium/high labels and errorStrategy 'retry' with dynamic task.attempt scaling instead of one giant request.channel.of(...), explicit closure params ({ v -> ... }), def for all variables, and emit:-named outputs. Check with nextflow lint.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/
Assess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take k-dense-ai/nextflow from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.
The instructions reference pip, uv.
Without those the skill loads but fails at the first command.