Code Review for Reproducibility AI Prompt

Review my analysis code for reproducibility and identify problems that would prevent another researcher from replicating my results.

Code: {{analysis_code}}
Language: {{language}}

Check for each category of reproducibility problem:

1. Environment problems (code may run differently on another machine):
   - Absolute paths: any path starting with /Users/ or C:\Users\ will fail on another machine. Replace with relative paths from the project root.
   - Missing package/library declarations: list all library() or import statements at the top of the script.
   - Undeclared package versions: are package versions recorded? Different versions may produce different results.
   - System-specific code: any code that depends on OS-specific behavior.
   - Missing random seeds: any analysis using randomization must set a seed for reproducibility.

2. Ordering problems (code must run from top to bottom without manual steps):
   - Objects used before they are defined: will cause errors if run sequentially.
   - External file dependencies not created by earlier code: scripts that depend on files that another analyst must manually provide.
   - Hidden state: code that relies on objects in the global environment from a previous session.
   - Manual steps: any step that requires human intervention (e.g. 'run this block first, then that block').

3. Data provenance problems:
   - Raw data modified in place: raw data files should never be overwritten.
   - Missing data source documentation: where did the raw data come from? How was it obtained?
   - Undocumented exclusions: data filtered or excluded without comment explaining why.

4. Documentation problems:
   - Uncommented analytical decisions: if a choice was made (which covariates to include, how to handle outliers), a comment should explain why.
   - Variable names that require knowledge of the project: use descriptive variable names.
   - No description of what the script does at the top.

5. Output stability:
   - Does the code produce the same output when run twice with the same inputs?
   - Are intermediate results saved so the full pipeline does not need to re-run to get the final results?

For each problem found:
- Line number or code section
- Description of the problem
- Corrected code

Return: annotated code review, corrected code, and a reproducibility score (0–100) with justification.