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python-model-code-generator

authentic

Generate and run minimal reproducible Python modeling code for the human-approved main method and usable baseline, saving compact experiment artifacts and a canonical run summary.

When to use

• Generate runnable Python modeling code
• Implement approved main method
• Implement usable baseline
• Save experiment artifacts
• Create canonical run summary
• Evaluate output-degeneracy metrics
• Record fallback-trigger metrics

When not to use

• Change approved model or baseline
• Read or overwrite raw data
• Emit placeholder metrics or figures
• Create one script per unapproved candidate
• Create README duplicating code plan
• Present Type 1 diagnostic figures as paper figures

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