/minimal-run-and-audit

name: minimal-run-and-audit
description: Rigor Run skill for README-first deep learning repo reproduction. Use when the task is specifically to capture or normalize evidence from the selected smoke test or documented inference or evaluation command and write standardized `repro_outputs/` files, including patch notes when repository files changed. Do not use for training execution, initial repo intake, generic environment setup, paper lookup, target selection, hidden scientific-meaning changes, or end-to-end orchestration by itself.

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`.