Research init

Skill brycewang-stanford/Auto-Empirical-Research-Skills/skills/61-phdemotions-research-methods/skills/research-init

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Install

npx -y skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill research-init

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One thing to look at

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What its author says it does

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Scaffold a new research project with full reproducibility infrastructure in R and/or Python. Creates directory structure, pipeline stubs (targets/Snakemake), environment lockfiles (renv/uv), documentation templates (codebook, decision log, pre-registration, Cornell README), Quarto manuscript template, and proper .gitignore. Can wrap existing data in gold-standard structure. Use when the user says "new project," "scaffold," "start a study," "set up a project," "I have data and need to organize it," or when /research-intake recommends scaffolding.

SKILL.md

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/research-init — Project Scaffolding

You create the structure that makes everything else possible. A well-scaffolded project is halfway to reproducibility before a single line of analysis is written.

How to scaffold a project

Step 1 — Gather requirements

Ask the researcher (or infer from context):

Project name — will become the directory name
Language — R, Python, or both? (Default: both)
Existing data? — If yes, where? What format? This changes the workflow.
Research question — one sentence, for the README and pre-registration skeleton
Target journal — if known, for formatting defaults

If the researcher provides a project name and says "scaffold it," don't over-ask. Use sensible defaults and get them started.

Step 2 — Create directory structure

Create the full structure documented in references/criteria.md. Use the templates in references/templates/ for each file.

Step 3 — Initialize environments

For R:

Create _targets.R from template
Initialize renv (if R is available on the system)
Create R/00_setup.R from template

For Python:

Create Snakefile from template
Create pyproject.toml with research stack dependencies
Create python/00_setup.py from template

Step 4 — Handle existing data

If the researcher has existing data:

Copy (not move) files to data/raw/
Set data/raw/ as conceptually read-only (the raw-data-guard hook enforces this)
Note the original file locations in the README provenance section
Suggest running /data-validate next

Step 5 — Initialize git

If not already in a git repo:

Create .gitignore from template
git init
Create initial commit with structure (but NOT data files — those go in .gitignore or are tracked separately)

Step 6 — Print summary and next steps

Show the researcher what was created and suggest next steps per _shared/next-steps.md.

Principles

Read references/principles.md for the foundational principles behind every scaffolding decision.

Voice

Efficient and organized. You're setting up a workspace, not giving a lecture. Create the structure, explain what each piece is for briefly, and get the researcher moving. Show the directory tree at the end so they can see what was built.

Argument handling

research-init my-study → creates ./my-study/
research-init my-study --lang r → R only
research-init my-study --existing-data ~/data/survey.csv → copies data to data/raw/
research-init (no args) → asks for project name