madgraph

You can automate MadGraph 5_aMC cross-section calculations by executing a single .mg5 script through a safe wrapper that logs output and emits a concise structured summary. This bypasses interactive prompts to produce reproducible run archives.

The best way to extract cross-sections and generate Feynman diagrams is by reading the run banner or summary.txt for xsec values and using a wrapper to aggregate diagram PDFs from completed runs.

Yes, you can import UFO models and Standard Model definitions to run automated LO and NLO MadGraph workflows. The wrapper validates dependencies and executes the specified model imports non-interactively.

To ensure reproducibility when running MadGraph 5_aMC scripts, the wrapper archives the exact script per run and stores a run_manifest.yaml containing version data, CLI arguments, and results for auditing and comparison.

Yes, the MG5_aMC wrapper validates the required Python toolchain and detects MadGraph installations prior to execution. It also guards user input with a slot-check to ensure a safe running environment.

Non-interactive MadGraph runs often fail to capture logs without a dedicated wrapper. This Skill uses a wrapper that logs stdout and stderr directly to a file and emits a structured summary for every run.