reproducibility-audit

To automate reproducibility audits, the Skill executes paper code in a sandbox environment, compares the outputs against the stated claims, and generates detailed reports. It detects code repositories and builds isolated environments to verify research results.

Sandbox code execution works by building an isolated environment to run academic paper experiments safely. This verifies research results by comparing the executed outputs directly against the claims made in the original academic paper.

Yes, you need Git and Python to assess reproducibility, along with libraries like NumPy, Pandas, and scikit-learn. These dependencies are required for fetching code repositories, executing experiments, and comparing outputs against paper claims.

You can verify machine learning paper results by executing the paper's code in a sandbox environment. The Skill utilizes NumPy, Pandas, and scikit-learn to run the experiments and accurately compare the generated outputs against the paper claims.

The best way to generate comparison reports is to run the academic paper's code in an isolated sandbox environment and automatically compare the execution outputs against the original claims. This process generates detailed reproducibility audit reports.

A limitation of running academic paper code in a sandbox is that it requires specific dependencies including Git, Python, NumPy, Pandas, and scikit-learn. Complex experiments lacking these standard libraries or requiring external data might not execute correctly in the isolated environment.