reproducible-pipelines

Name: reproducible-pipelines
Availability: InStock
Author: james-traina

Automate reproducible research pipeline setup with Make, Snakemake, or DVC workflows.

11|2|Updated Feb 26, 2026

Search Tags:#research #pipeline #workflows #Makefile #reproducible #Snakemake #DVC

Ready to run

npx skills add https://github.com/James-Traina/compound-science --skill reproducible-pipelines

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: reproducible-pipelines
Source: https://github.com/James-Traina/compound-science/tree/main/skills/reproducible-pipelines
Command: npx skills add https://github.com/James-Traina/compound-science --skill reproducible-pipelines

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guides researchers in building and maintaining reproducible pipelines from project skeletons to fully documented replication packages.

Core Features & Use Cases

Standard project directory layouts and automation for Make, Snakemake, and DVC workflows
Environment management and dependency versioning (conda, Docker, renv) to ensure regenerable results
Replication package preparation with data citations and AEA data editor readiness

Quick Start

Create a complete reproducible pipeline setup by defining a standard project structure and configuring a Makefile or Snakemake workflow.

Frequently Asked Questions about reproducible-pipelines

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I build a reproducible research pipeline from scratch?▼

Build a reproducible research pipeline by applying a standardized repository structure, configuring a Makefile or Snakemake workflow, and provisioning environments with conda or Docker to ensure dependency management and regenerable results.

What's the best way to prepare a replication package for the AEA data editor?▼

Prepare an AEA-ready replication package by generating a complete workflow with data citations, a comprehensive README, and explicit environment specifications that document data provenance and dependencies.

Does this pipeline setup support both DVC and Snakemake workflows?▼

Yes, the pipeline setup supports both DVC and Snakemake workflows, alongside Make, by applying standard directory layouts and automation rules tailored to each framework's specific requirements.

How do I manage environments and dependencies for an empirical research project?▼

Manage environments for empirical research projects using conda, Docker, or renv to provision dependencies, version control data, and ensure that computational results remain fully regenerable.

When do I need a Makefile versus Snakemake for reproducible workflows?▼

You need a Makefile for standard dependency resolution and shell automation, whereas Snakemake is suited for complex, scalable bioinformatics or data-intensive workflows requiring reproducible pipeline management.

Can I add reproducible workflows to an ongoing empirical study?▼

Yes, you can add reproducible workflows to ongoing empirical studies by enforcing standardized repository structures, precise dependency management, and data provenance tracking without starting a new project skeleton.