version: "1.0.0" name: minimal-run-and-audit description: Trusted-lane execution and reporting skill for README-first AI 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, or end-to-end orchestration by itself.

minimal-run-and-audit

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.

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
• clear distinction between verified, partial, and blocked states
• PATCHES.md when repo files changed

Notes

Use references/reporting-policy.md, scripts/run_command.py, and scripts/write_outputs.py.