# snakemake/snakemake

This is the development home of the workflow management system Snakemake. For general information, see

Repository: https://github.com/snakemake/snakemake
Canonical: https://ross.abutalabs.com/products/snakemake
Homepage: https://snakemake.github.io
Language: Python
License: MIT
License Family: permissive
Topics: snakemake, reproducibility, workflow-management
Last push: 2026-08-26T08:26:22+00:00

## Health v2 (maintenance only)
Score: 95/100 (v2, computed 2026-09-03T02:39:23.370411+00:00)
- activity 99, release rhythm 87, longevity 100
- inputs: {"age_days": 2525, "days_push": 7, "days_rel": 7, "gap_med": 3.0, "n_releases_24m": 123}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2853, forks 654 (observed 2026-08-28T04:07:25.645265+00:00)

## What it is
Snakemake is a Python-based workflow management system for creating reproducible and scalable data analyses. Workflows are defined via a readable rule-based language and can scale from laptops to clusters, grids, and clouds with automatic software deployment via Conda and containers.

## Use cases
- build reproducible data analysis pipelines
- run bioinformatics workflows on a cluster
- scale a pipeline from laptop to cloud without changing code
- manage multi-step ETL workflows with dependencies
- automatically deploy software dependencies for each workflow step
- create reusable workflow modules and wrappers
- orchestrate genomics analysis pipelines

## When to choose
- you need reproducible, scalable scientific or data analysis pipelines
- you want Python-based workflow syntax with shell, script, and notebook integration
- you need to run the same workflow on a laptop, HPC cluster, or cloud
- you want automatic deployment of per-step software environments via Conda or containers

## When to avoid
- you need a simple one-off script with no multi-step dependencies
- your team prefers YAML-only pipeline definitions without Python
- you need real-time streaming data processing rather than batch workflows
- you want a fully declarative DAG tool with no programming

## Facets
- artifact type: framework
- maturity: stable
- function: workflow-automation, etl, deployment, cli
- domain: data-science, bioinformatics, developer-tools
- platform: python, cli, cloud, cross-platform
- tags: reproducibility, scientific-workflows, pipeline-management, bioinformatics, conda-integration, containerization, job-scheduling, data-engineering, automation, linux, macos, docker

## Member repositories
- snakemake/snakemake (main) score 95

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:25.645265+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T07:36:52.558898+00:00, confidence not recorded.
  - readme: https://github.com/snakemake/snakemake (fetched 2026-08-28T04:07:25.645265+00:00, sha 35182f1479fa)
  - homepage: https://snakemake.github.io (fetched 2026-08-29T09:52:26.029161+00:00, sha 31a15135a794)
- Data as of 2026-08-30T08:39:29.467469+00:00.