Python-based workflow manager for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake resolves execution order and parallelism. Runs local, SLURM, LSF, AWS, GCP via profiles; per-rule conda/Singularity envs. For NGS pipelines, ML training, and multi-step file processing. Use Nextflow for Groovy dataflow or nf-core integration.
NewMaintained
Automatic
◆Data Engineer pack
Last verified 8/24/2026
What it changes
Passed 0 of 22 test cases with the skill, and 22 of 22 without it.
Without skill100%
With skill0%
0 fixed20 passed either way1 still failing1 broke
Pass-rate lift
-100 pp
Tokens
+482%
Agent turns
0%
Response time
-7%
Model gemini-3.6-flashMeasured 8/24/2026v120 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
What's inside
The instructions your model reads when the skill loads.
SKILL.md557 linessingle file
Snakemake — Python Workflow Engine
Overview
Snakemake is a Python-based workflow management system that scales analyses from laptop to HPC and cloud. Workflows are defined as rules with explicit input/output file dependencies; Snakemake resolves the execution order automatically and runs independent steps in parallel. Rules can call shell commands, Python/R/Julia scripts, or inline Python. Per-rule conda or Singularity environments make workflows fully reproducible. Widely used in bioinformatics for NGS, genome assembly, and variant-calling pipelines.
Scaling the same workflow from local development to SLURM cluster without code changes
Processing multiple samples identically using wildcard-based rules
Managing dependencies automatically — only rerun steps whose inputs changed
Deploying per-rule conda or Singularity environments for tool isolation
Generating visual DAGs and dry-run previews before committing computational resources
Use Nextflow instead when you need Groovy DSL + dataflow channels, or when leveraging the nf-core community pipeline library
For simple shell loops, use bash scripts; Snakemake is worth the overhead only for 3+ sequential steps with branching
Use Prefect or Airflow instead for data engineering workflows with dynamic task graphs or time-based scheduling
Prerequisites
Python packages: snakemake, graphviz (for DAG visualization)
Environment: Python 3.11+; conda/mamba recommended for per-rule environments
Data requirements: Input files, reference files; output paths defined as rules
Check before installing: The tool may already be available in the current environment (e.g., inside a pixi / conda env). Run command -v snakemake first and skip the install commands below if it returns a path. When running inside a pixi project, invoke the tool via pixi run snakemake rather than bare snakemake.