minimal-run-and-audit
Installation
Summary
Standardized execution and audit reporting for deep learning repository reproduction runs.
- Captures evidence from smoke tests, inference runs, and evaluation commands; writes normalized outputs to
repro_outputs/with patch tracking when repository files change - Generates
SCIENTIFIC_CHANGELOG.mdto document changes affecting evaluation, preprocessing, or metrics, andCOMPARABILITY_REPORT.mdto assess alignment with README and paper baselines - Applies only after a reproduction target and setup plan exist; does not handle initial repo intake, training execution, or target selection
- Distinguishes between verified, partial, and blocked execution states; refuses to hide changes that alter scientific meaning
SKILL.md
minimal-run-and-audit
Use the shared operating principles in
../../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.
Installs
139.7K
Repository
lllllllama/ai-paper-reproduction-skillGitHub Stars
412
First Seen
Mar 30, 2026
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