Build, run, and debug Nextflow pipelines and nf-core workflows.
npx skills add https://github.com/dralkh/seerai --skill nextflow-dralkh
Please help me install this Agent Skill. Skill: nextflow Source: https://github.com/dralkh/seerai/tree/main/skills/nextflow Command: npx skills add https://github.com/dralkh/seerai --skill nextflow-dralkh
This Skill helps you design, run, and troubleshoot reproducible Nextflow pipelines without getting lost in workflow syntax, container setup, or execution-profile details.
Ask me to run, debug, or build a Nextflow pipeline and provide the workflow files, inputs, and target environment.
High-intent search queries and answers about installing and using this skill.
To run and debug a Nextflow pipeline, apply the correct config profiles, parameters, and resume behavior to execute end to end. Configure containers, executors, and resources to ensure reproducible operations across local machines, HPC, or cloud environments.
Writing DSL2 modules involves defining processes and wiring them together using Nextflow channels. You build reproducible workflows by organizing code into modules and subworkflows, ensuring proper process-channel wiring for end-to-end data pipelines.
Troubleshooting Nextflow caching and resume failures requires resume-aware debugging using tracing, reports, and cache analysis. Analyze execution logs to find failures, validate changes with nf-test, and restart the data pipeline with the correct resume behavior.
Yes, you can use nf-test for validating changes and linting Nextflow workflows. Apply nf-test-based development alongside tracing and reports to find failures, test DSL2 modules, and ensure your data pipeline behaves reproducibly across different runs.
Configuring containers and executors for nf-core workflows requires setting up nextflow.config files with appropriate profiles. Pin runtimes and configure resources to ensure reproducible operations whether running on local machines, HPC, or cloud executors.
Running nf-core workflows like RNA-seq requires a properly formatted samplesheet and input FASTQ files. The pipeline processes these inputs into validated, containerized outputs by applying the correct parameters, config profiles, and pinned runtimes.
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