| name | replication-driven-research |
| description | Use when starting empirical analysis, creating a data pipeline, generating results, or when data or model specifications change. Enforces end-to-end reproducibility — every number in the paper must be regenerable from raw data by a script with a fixed seed. Replaces TDD for the research domain. |
Replication-Driven Research
Overview
This skill is the research-domain analogue of test-driven development. The core philosophy is the same as TDD: evidence before claims, automated verification, invalidation on input change. No result is valid until the pipeline runs end-to-end without error. No number enters the paper until a script regenerates it from raw data. This is the single most important discipline in the superpapers plugin, and every analysis skill defers to it.
When to Use
- Start of a new empirical project
- First time running analysis code in a project
- Before generating any table or figure for a paper
- After any change to raw data, sample selection, or model specification
- Before declaring a result "final" or "verified"
- When the user asks "is this reproducible?"
- When reviewer comments require re-running the analysis
Canonical Directory Structure
The canonical project layout is:
project-root/
├── data/
│ ├── raw/ # raw downloads, never manually edited
│ ├── processed/ # cleaned data, output of scripts
│ └── manifest.md # documents every dataset (source, URL, date, variables)
├── code/
│ ├── 01_collect.R # or .py — fetches raw data
│ ├── 02_clean.R # raw → processed
│ ├── 03_analyze.R # processed → results
│ └── 04_figures.R # processed → figures
├── output/
│ ├── tables/ # .tex files generated by scripts
│ ├── figures/ # .pdf vector files generated by scripts
│ └── logs/ # execution logs with timestamp + seed
├── paper/
│ ├── paper.tex # main document
│ ├── references.bib # bibliography
│ └── sections/ # split sections if needed
└── CLAUDE.superpapers.md # project settings
