Automate creation and debugging of Snakemake workflows for SLURM-based HPC.
npx skills add https://github.com/sahuno/llm_configs --skill snakemake
Please help me install this Agent Skill. Skill: snakemake Source: https://github.com/sahuno/llm_configs/tree/main/claude/skills/snakemake Command: npx skills add https://github.com/sahuno/llm_configs --skill snakemake
Snakemake on HPC often requires careful configuration to achieve reproducible, scalable workflows under SLURM with proper container usage, resource management, and error handling. It provides a standardized approach to building, debugging, and running per-sample, DAG-driven bioinformatics pipelines with SLURM executor plugins.
Run Snakemake with a config.yaml and Snakefile on a SLURM cluster to deploy a per-sample DAG with containers and a dry-run.
High-intent search queries and answers about installing and using this skill.
You can build reproducible Snakemake workflows for SLURM HPC by using Snakemake 9+ with slurm executor plugins. This involves creating per-sample DAGs, pinning container images, and using run_snakemake.sh templates to organize multi-sample bioinformatics pipeline runs.
Integrating containers in a Snakemake pipeline on SLURM requires pinning container images within the workflow definition. This ensures reproducible execution across the HPC cluster by enforcing consistent software environments for every step of the DAG.
Snakemake workflow failures on SLURM clusters often stem from missing slurm executor plugins or improper resource management. You can resolve these by applying debugging patterns for common pitfalls, validating resource requests, and performing dry-run checks before submission.
Yes, Snakemake 9+ is required to properly utilize slurm executor plugins for HPC execution. This version ensures compatibility with the standardized approach for building per-sample DAGs and managing reproducible containerized runs on SLURM.
Yes, you can use Snakemake dry-run checks to validate a per-sample DAG before executing it on HPC. This process verifies the workflow structure, ensures run metadata is correct, and identifies potential errors without submitting jobs to the SLURM cluster.
You organize multi-sample bioinformatics pipelines in Snakemake by building per-sample DAGs and utilizing run_snakemake.sh templates. This method enforces run metadata and reproducible outputs, ensuring scalable workflow execution on SLURM-based HPC systems.
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