Minimal Run And Audit

# minimal-run-and-audit

Use this as the Rigor Run skill. The installed slug remains `minimal-run-and-audit` for compatibility.

Use the shared operating principles in `../ai-research-reproduction/references/agent-operating-principles.md`; this skill should make run evidence auditable without turning every command into a rigid protocol.

## When to apply

- After a reproduction target and setup plan exist. - When the main skill needs execution evidence and normalized outputs. - When a smoke test, documented inference run, documented evaluation run, or other short non-training verification is appropriate. - When the user already knows what command should be attempted and wants execution plus reporting only.

## When not to apply

- During initial repo scanning. - When environment or assets are still undefined enough to make execution meaningless. - When the task is a literature lookup rather than repository execution. - When the user is still deciding which reproduction target should count as the main run.

## Clear boundaries

- This skill owns normalized reporting for an attempted command. - It may receive execution evidence from the main skill or a thin helper. - It does not choose the overall target on its own. - It does not perform broad paper analysis. - It does not own training startup, resume, or long-running training state. - It should not normalize risky code edits into acceptable practice. - It must not hide changes that alter evaluation, preprocessing, checkpoints, metrics, or other scientific meaning.

## Input expectations

- selected reproduction goal - runnable commands or smoke commands - environment and asset assumptions - optional patch metadata

## Output expectations

- execution result summary - standardized `repro_outputs/` files - `SCIENTIFIC_CHANGELOG.md` for changed scientific meaning and evidence status - `COMPARABILITY_REPORT.md` for README/paper/baseline comparability - clear distinction between verified, partial, and blocked states - `PATCHES.md` when repo files changed

## Notes

Use `references/reporting-policy.md`, `../ai-research-reproduction/references/research-rigor-principles.md`, `scripts/run_command.py`, and `scripts/write_outputs.py`.