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/runcommand.py, and scripts/writeoutputs.py.