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Repository Memory for AI Code Reviews

Quick Review Commands

When asked to "run code review" or "review code", use these prompts:

Standard Code Review

You are a code reviewer. Find any bugs in the code in this repository in the [TARGET_DIRECTORY] directory.

Comprehensive Python Review

Search through all Python files and look for:
1. Import errors or missing modules
2. Variable naming inconsistencies 
3. File path issues (hardcoded paths, missing directories)
4. Data type mismatches or conversion errors
5. Index/column name mismatches in pandas operations
6. Division by zero or NaN handling issues
7. Logic errors in conditionals
8. Missing error handling
9. Inefficient or problematic code patterns

Report Generation

Always end reviews with:

write the error report to a file AI-Review.md

Then add statistics:

Add to the report the total numbers of tokens used for the review, and the approximate cost of this session.

Repository Context

  • This is a research/academic repository
  • Contains Python data analysis code
  • Uses MATLAB for modeling
  • Focus on data science and financial analysis code patterns