replication-package

name	replication-package
description	Scaffold or audit a social-science replication package at a target directory. Generates folder structure, README, master.R, figure/table crosswalk, codebook template, LICENSE placeholder, and pre-release checklist. Adapted from Yusaku Horiuchi's replication-package-guide with FAIR-principle integration; platform-neutral (Harvard Dataverse, OSF, Zenodo, GitHub releases, institutional archives).
argument-hint	[path to replication folder; defaults to ./replication]
allowed-tools	["Read","Write","Edit","Bash"]

Replication Package Scaffold

Heritage and attribution

The structural conventions in this skill (single-entry-point principle, compact vs. build/analyze layouts, figure/table crosswalk, paper-consistency check, correction workflow, pre-release checklist) come from Yusaku Horiuchi's replication-package-guide. Horiuchi's repository README explicitly authorizes AI consumption: it is "designed to be read by humans and by coding agents such as Codex or Claude Code before they prepare, audit, or repair a replication package."

This skill is a modification, not a copy.

• Repackaged as procedural guidance for Claude Code (frontmatter, step-by-step instructions, quality checks).
• Folded in the FAIR principles (Findable, Accessible, Interoperable, Reusable; Wilkinson et al. 2016; GO FAIR) so the scaffolded package is platform-neutral.
• Dropped platform-specific upload mechanics. This skill builds and audits the local package. Uploading to Harvard Dataverse, OSF, Zenodo, a journal repository, or an institutional archive is left to the user and the platform's tools.
• Reorganized templates and checklists into a single self-contained skill.

Horiuchi's own caveat applies: "AI is useful for checking, reorganizing, documenting, and catching inconsistencies, but it should not be treated as a substitute for the author's judgment about which files, scripts, data sources, and results are actually part of the replication record." Use this skill as an assistant, not as a substitute for the author's judgment about what belongs in the public package.

If you publish a package built with this skill, cite Horiuchi's guide as the methodological source.

Standard

A replication package is ready when a competent reader can download it, open the package root, run one documented command, and regenerate the published results without hidden manual steps.

Minimum standard:

• One public entry point (master.R by convention; run_replication.R acceptable when that is the project convention).
• One authoritative README.md.
• Relative paths only.
• Public data inputs, or clear restricted-data instructions.
• Codebook or data dictionary for every analysis-ready dataset.
• Figure/table crosswalk in paper order.
• Logs that record inputs, sample sizes, warnings, and session information.
• Public scripts that are numbered or otherwise ordered.
• No personal files, caches, credentials, or obsolete exploratory scripts in the public path.

Instructions

Step 1. Resolve the target directory

Use $ARGUMENTS if provided. Treat the argument as the path to the replication folder (relative or absolute). If the argument is empty, ask the user once for a path. If they decline, default to ./replication relative to the current working directory.

Normalize the path. Confirm whether the directory exists and whether it is empty.

Step 2. Decide on structure

Ask the user one question. Is data construction complex (restricted sources, scraping, API pulls, or expensive upstream work that produces analysis-ready data)?

• No → use compact.
• Yes → use build/analyze.

When in doubt, choose compact. Build/analyze is justified only when the build stage creates real complexity for users.

Step 3. Decide between scaffold and audit

• If the target directory is empty or does not exist → scaffold mode. Create the directory if needed, write the full skeleton.
• If the target directory contains files → audit mode. Read everything, compare against the pre-release checklist, report what is present, partial, or missing. Offer to fill in only the missing scaffolding (files that do not yet exist). Never overwrite an existing file without explicit user confirmation.

Step 4. Scaffold the tree

Compact structure (default):

<root>/
|-- README.md
|-- master.R
|-- LICENSE
|-- .gitignore
|-- data/
|-- code/
|-- docs/
|   `-- crosswalk.md
`-- outputs/
    |-- figures/
    |-- tables/
    `-- logs/

Build/analyze structure:

<root>/
|-- README.md
|-- master.R
|-- LICENSE
|-- .gitignore
|-- build/
|   |-- data/
|   |-- scripts/
|   `-- output/
`-- analyze/
    |-- data/
    |-- scripts/
    |-- figures/
    |-- tables/
    |-- docs/
    |   `-- crosswalk.md
    `-- logs/

Create the directories first, then write the template files in Step 5. Leave data/, code/, scripts/, figures/, tables/, and logs/ empty (the user fills them with project content).

Step 5. Write template files

Use the templates in the Templates section below. Fill in placeholder fields (<paper title>, <authors>, etc.) with values the user provides; if a placeholder cannot be resolved from context, leave it as written and flag it in the final report so the user knows what to edit.

Step 6. Report

After scaffolding, output a short report with:

1. The directory tree created (or the audit diff for audit mode).
2. A list of placeholder fields the user must fill in.
3. The next three actions the user should take (typically: fill in README placeholders, drop data into data/, add scripts under code/ or build/scripts/ and analyze/scripts/).

Templates

README.md

# <paper title>

**Authors.** <author 1>, <author 2>, ...

**Journal.** <journal name>, <year>. DOI: <article DOI>

**Data DOI.** <data archive DOI>

**Verified.** <YYYY-MM-DD>

## What this package reproduces

<one paragraph: which figures, tables, and in-text numbers this package generates from which data.>

## How to run

From a fresh R session in the package root:

```r
source("master.R")

master.R runs the full public path end-to-end and writes session information and per-script logs to outputs/logs/ (compact) or analyze/logs/ (build/analyze).

Software requirements

• R
• Required packages:
• Operating system tested on:
• Approximate runtime on the listed environment:

A session_info.log is written by master.R on a successful run and records the exact package versions used.

Folder structure

<paste the actual tree from tree -L 2 or list manually>

Data sources

• <dataset 1> — <source, license, public or restricted, citation>.
• <dataset 2> — ...

If any input is restricted, document how a reader with access can obtain it and which files in this package depend on it.

File descriptions

• master.R — public entry point.
• code/01_*.R — .
• code/02_*.R — .
• data/<file>.csv — <one-line description; see docs/codebook.md for variables>.
• docs/crosswalk.md — paper-order map from figures/tables to scripts and outputs.
• outputs/figures/, outputs/tables/, outputs/logs/ — generated by master.R.

Figure and table crosswalk

See docs/crosswalk.md. Every figure and table in the paper and its appendix appears there with the script that generates it and the output path.

Citation

License

See LICENSE. <one sentence: data license, code license, any restrictions>.

Attribution

This package follows the structural conventions in Yusaku Horiuchi's replication-package-guide and the FAIR principles (Wilkinson et al. 2016, doi:10.1038/sdata.2016.18).

### `master.R`

```r
# master.R — public entry point for <paper title> replication package.
# Running this script regenerates every figure, table, and reported number
# from the public input data.

# Reproducibility
set.seed(20260101)              # change to the seed used in the paper
options(stringsAsFactors = FALSE)

# Capture the start time and prepare the log directory
.start_time <- Sys.time()
log_dir <- "outputs/logs"        # change to "analyze/logs" if build/analyze
if (!dir.exists(log_dir)) dir.create(log_dir, recursive = TRUE)

# Run scripts in order. Add or remove as the project grows.
source("code/01_load.R")         # load and validate inputs
source("code/02_clean.R")        # clean and recode
source("code/03_analysis.R")     # estimate models
source("code/04_figures.R")      # produce figures
source("code/05_tables.R")       # produce tables

# Session info
writeLines(
  capture.output(sessionInfo()),
  file.path(log_dir, "session_info.log")
)

# Runtime
.end_time <- Sys.time()
cat(
  sprintf("Replication complete. Elapsed: %s.\n",
          format(round(.end_time - .start_time, 2)))
)

docs/crosswalk.md

# Figure and Table Crosswalk

In paper order. Every figure and table in the article and supplementary information must appear in this table. Mark conceptual or hand-made items explicitly.

| # | Type | Label / Caption (short) | Script | Output path |
|---|------|-------------------------|--------|-------------|
| 1 | Figure | <short caption> | `code/04_figures.R` | `outputs/figures/fig01.pdf` |
| 2 | Table | <short caption> | `code/05_tables.R` | `outputs/tables/tab01.tex` |
| 3 | Figure (conceptual) | <short caption> | — | `docs/concept_fig.pdf` (hand-drawn; not generated) |

docs/codebook.md

# Codebook

One entry per public analysis-ready dataset. List every variable.

## `data/<dataset>.csv`

Source: <where this dataset comes from; raw input, derived, or restricted>.
N rows: <count>.
N cols: <count>.

| Variable | Type | Values / range | Description |
|----------|------|----------------|-------------|
| `id` | integer | 1–N | Respondent identifier. Anonymized. |
| `treatment` | factor | control / T1 / T2 | Experimental assignment. |
| `outcome` | numeric | 0–100 | Primary outcome (see paper §2.1). |

LICENSE

# LICENSE — fill this in before publishing.
#
# Common choices for replication materials:
#  - Code: MIT, BSD-3-Clause, or Apache-2.0.
#  - Data: CC0 (waiver) for fully public data, or CC BY 4.0 for attribution-required.
#  - Whole package: CC BY 4.0 is a common single-license choice when code and data ship together.
#
# Restricted-data files cannot be licensed here. Document them in the README.
#
# Replace this file with the chosen license text. Update the README's License section to match.

.gitignore

# OS
.DS_Store
Thumbs.db

# Editors
.vscode/
.idea/
*~

# R
.Rhistory
.RData
.Ruserdata
.Rproj.user/
*.Rcheck/
*.tar.gz

# Python
__pycache__/
*.pyc
.venv/
venv/

# Secrets and local config
.env
.env.*
*.pem
*.key

# Logs from local runs that should not be committed
*.tmp

# Large generated artifacts; comment out if outputs should be tracked
# outputs/figures/*.pdf
# outputs/tables/*.tex

Pre-Release Checklist

Run this after scaffolding is done and the user has filled in placeholders, dropped in data, and written scripts.

• The package runs in a clean temporary directory.
• One public entry point (master.R) runs the full public path with one command.
• All paths are relative.
• README.md is current and matches the files on disk.
• Data dictionaries in docs/codebook.md match the public datasets.
• Figure/table crosswalk in docs/crosswalk.md is complete and in paper order.
• Logs and session_info.log are produced by master.R on a successful run.
• No credentials, tokens, personal paths, caches, or exploratory files in the public path.
• Restricted data, if any, are documented in the README.
• LICENSE is filled in.
• The final archive has been downloaded from the destination repository and re-run from a clean directory.

The repository copy is the truth. A local run is necessary but not sufficient.

Paper Consistency Check

When the manuscript source or final PDF is available, verify:

• Every figure and table cited in the paper and appendix appears in docs/crosswalk.md.
• Every generated figure or table path in the crosswalk exists on disk.
• All in-text sample sizes, estimates, confidence intervals, p-values, field dates, and descriptive numbers can be traced to logs, scripts, generated tables, or generated figures.
• Conceptual or hand-made items are marked as such in the crosswalk.
• The public archive reproduces the figures and tables actually reported in the published article.

If paper source files cannot be included publicly, document whether they were used during package preparation.

When to reach for this skill vs. siblings

• replication-package (this) — scaffold or audit a replication package at a target directory before upload to any repository.
• fair-check — audit a finished manuscript and its accompanying package against the FAIR principles end-to-end. Use after this skill, before submission.
• methods-reporting — check that the manuscript's methods section reports what the package documents (CONSORT, JARS, DA-RT).

Quality Checks

• Target directory resolved before any file is created.
• Structure choice (compact vs. build/analyze) made explicitly with the user, not inferred silently.
• Existing files in the target directory are never overwritten without explicit user confirmation.
• All scaffolded files use relative paths.
• README.md template includes every required section: paper title, authors, journal and DOI, data DOI, verification date, what the package reproduces, run instructions, software requirements, folder structure, data sources, file descriptions, crosswalk reference, citation, license, attribution.
• master.R skeleton writes session_info.log and a runtime line on success.
• docs/crosswalk.md template is in paper order with separate columns for number, type, short caption, generating script, and output path.
• docs/codebook.md template includes one entry per analysis-ready dataset.
• .gitignore excludes .env, *.pem, *.key, editor caches, R session artifacts, Python caches, and OS metadata files.
• No Harvard Dataverse, OSF, or other platform-specific upload code is generated. This skill stops at the local package boundary.
• Attribution to Horiuchi's replication-package-guide is preserved in the generated README.md and is visible in this skill's heritage section.
• Final report lists placeholder fields the user must edit before publishing.