Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.
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Review research code for reproducibility and quality, extract the paper's main empirical claims, compare paper to code, and write a constructive markdown report. Designed for social science / economics projects with LaTeX papers and Stata, R, or Python code.
Review a research project's paper and code for reproducibility, code quality, and paper-code alignment. Be constructive, concrete, and calibrated. Treat gaps as items to verify, not accusations.
Scope
This skill supports:
LaTeX papers
Stata (.do), R (.R, .r), and Python (.py) code
Default review depth:
main: prioritize the main paper, main scripts, and core outputs
full: inspect all detected code files in scope
If no depth is provided, default to main.
Phase 1: Discover the Project
First parse $ARGUMENTS:
If one argument looks like a .tex path, use it as PAPER_FILE.
If one argument looks like a directory path, use it as CODE_DIR.
If one argument is main or full, use it as REVIEW_DEPTH.
If any of the above are missing, auto-detect them.
1. Find the paper
Use Glob to search for **/*.tex, excluding obvious build folders such as _minted-*, build/, output/, .git/, node_modules/.
Identify the main paper file as the best candidate containing \documentclass or \begin{document}.
If multiple candidates exist, prefer:
A path explicitly provided in $ARGUMENTS
A file in Writing/, writing/, Paper/, paper/, Draft/, or the repo root
The file that appears to include the most component files via \input{} / \include{}
Record the result as PAPER_FILE.
2. Find the code
If CODE_DIR was not provided, look for likely code roots in this order:
Code/
Analysis/
code/
analysis/
scripts/
src/
programs/
replication/
If no single directory is clearly best, use the repo root and limit later discovery to likely code files.
Record the result as CODE_DIR.
3. Find code files
Within CODE_DIR and subdirectories, find:
**/*.do
**/*.R
**/*.r
**/*.py
Exclude obvious caches, environments, and generated folders where appropriate.
If REVIEW_DEPTH = main, prioritize:
Master scripts such as main.do, master.do, run_all.R, main.R, main.py, run.py
Files referenced by those scripts
Files that generate tables, figures, or final datasets
If no master script exists, select the most central files and cap the initial review set at a reasonable number
If REVIEW_DEPTH = full, include all detected code files.
Record:
CODE_FILES_ALL
CODE_FILES_REVIEWED
languages present
4. Find supporting documentation
Look for:
README.md, README.txt, readme.md
requirements.txt, environment.yml, pyproject.toml
renv.lock, DESCRIPTION
Record relevant files as available.
5. Handle ambiguity gracefully
If you find a paper and at least some code, continue even if discovery is imperfect.
Only stop if you cannot find either:
a main paper file, or
any relevant Stata, R, or Python code files
If you stop, tell the user briefly what was missing and what paths they can pass explicitly.
Before proceeding, tell the user:
the paper file chosen
the code directory chosen
the number of code files detected and the number selected for review
the review depth
any ambiguity worth noting
Phase 2: Read the Paper
Read PAPER_FILE.
Recursively read files referenced by:
\input{}
\include{}
\subfile{}
Extract a compact working summary for later cross-checking:
Paper title
Main research question
Main sample description
Main data sources
Main dependent variables
Main explanatory variables or treatments
Main estimation methods
Fixed effects and clustering, if stated
Main sample restrictions
Main tables and figures only
Headline quantitative claims only
Do not try to extract every statistic in the paper. Prioritize the main empirical design and the outputs most likely to map to code.
Store this as PAPER_SUMMARY.
Phase 3: Launch 2 Agents in Parallel
In a single message, launch both agents using the Agent tool with subagent_type: "general-purpose".
Each agent must produce a compact, high-signal output. Do not ask for exhaustive per-file prose on every file unless the project is very small.
AGENT A: Code Reproducibility and Quality
Store as CODE_REVIEW_SUMMARY.
Prompt:
You are reviewing research code for reproducibility and code quality in a social science / economics project.
