code-audit

To audit Python code for correctness and reproducibility, this Skill validates logic, edge cases, data handling, and relative path usage. It evaluates variable naming and dependency documentation to ensure scripts run reliably across different machines.

Yes, you can check an R script for data handling errors. The audit reviews how missing values, data types, and filtering are managed, while also verifying output accuracy and adherence to analytical best practices.

Yes, the code review tool supports Stata and Excel with formulas. It audits submissions across these specific languages for output verification, logic validation, and overall code organization to ensure reliability.

To ensure your Jupyter notebook is reproducible, this Skill verifies random seeds, relative paths, and dependency documentation. It assesses style, clarity, and adherence to best practices for understandable analytical work.

The best way to verify output accuracy is by auditing the code against its claims. This process identifies logic errors, validates edge case handling, and checks data type management to prevent analytical errors.

You need a reproducibility assessment when submitting analytical work to prevent errors on different machines. It verifies relative paths, random seeds, and dependency documentation to ensure code runs reliably and remains understandable.