replication-checker

name	replication-checker
description	Use when asked to verify reproducibility, audit a replication package, check a clean run, validate run instructions, or review before sharing research code.

Replication Checker

This is a clean-room reproducibility checklist for economics research projects.

Checklist

1. Read CLAUDE.md, the example or project README, and any data source map.
2. Identify the documented entry point and the smallest safe verification command.
3. Check path hygiene: no /Users/, C:\, machine names, or hidden local data assumptions in runnable code.
4. Check dependencies: requirements are declared and match imports.
5. Check data documentation: source, unit of observation, sample restriction, transformations, and caveats are visible.
6. Check generated outputs: outputs are either reproducible or clearly ignored.
7. Run the smallest safe command only when the user or task allows execution.
8. Return GREEN only if runnable evidence supports it; otherwise use YELLOW or RED.

Card-Krueger Baseline

Expected verification commands from the repository root:

python3 examples/card-krueger/src/did_analysis.py
python3 -m pytest examples/card-krueger/tests

Output

# Replication Report

**Status:** GREEN / YELLOW / RED

## Evidence

| Criterion | Status | Evidence |
| --- | --- | --- |

## Blockers

## Recommendations

## Commands

Constraints

• Do not mark GREEN without command output or equivalent evidence.
• Do not modify data files during an audit.
• Do not treat a successful synthetic-data run as a substantive replication of Card and Krueger (1994).