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Machine Learning · Testing / AGENT SKILL
minimal-run-and-audit
0
Capture standardized evidence from documented deep learning smoke tests, inference, or evaluation runs.
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.
Understand the trade-offs.
SKILL QUALITY
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The full skill.
Original instructions from the publisher’s SKILL.md
# 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`.
Skill folder
Files included alongside SKILL.md in the publisher’s repository.