minimal-run-and-audit
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.
适合你，如果需要严格复现深度学习仓库并记录标准化输出。
npx oh-my-skill add lllllllama/ai-paper-reproduction-skill/minimal-run-and-auditcurl -fsSL https://oh-my-skill.com/install.sh | bash -s -- lllllllama/ai-paper-reproduction-skill/minimal-run-and-auditnpx oh-my-skill verify lllllllama/ai-paper-reproduction-skill/minimal-run-and-audit怎么用
商店整理自技能原文 · 版本 56bc41c · 表述以原文为准装上后，Claude 会执行你指定的命令（如测试或推理），并把结果整理成标准化的 `repro_outputs/` 文件，包括运行摘要、科学变更记录和可比性报告。如果仓库文件被修改，还会生成补丁说明。
当你已经选好要复现的目标，并且知道要运行什么命令时，Claude 会接管执行并生成标准化报告。
技能原文 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 ../../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.mdfor changed scientific meaning and evidence statusCOMPARABILITY_REPORT.mdfor README/paper/baseline comparability- clear distinction between verified, partial, and blocked states
PATCHES.mdwhen repo files changed
Notes
Use references/reporting-policy.md, ../../references/research-rigor-principles.md, scripts/run_command.py, and scripts/write_outputs.py.