Reproducible Pipelines

Reference for building reproducible research pipelines: from project directory structure to automated workflows to journal-ready replication packages. Every computational result should be regenerable from raw data by running a single command.

When to Use This Skill

Use when the user is:

• Setting up a new empirical research project
• Building or debugging a Makefile/Snakemake/DVC pipeline
• Preparing a replication package for journal submission
• Managing computational environments (conda, Docker, renv)
• Tracking data provenance or versioning large datasets
• Debugging "works on my machine" reproducibility failures

Skip when:

• The task is about estimation methodology (use causal-inference or structural-modeling skill)
• The task is git workflow management (see workflows-work/references/worktree-patterns.md)
• The task is about orchestrating Claude agents (see slfg/references/orchestration-patterns.md)