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.
How do I install this agent skill?
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill --skill minimal-run-and-auditIs this agent skill safe to install?
- Gen Agent Trust Hubwarn
This skill is designed to execute commands for repository reproduction and audit the results. It features dynamic execution capabilities, such as loading shared modules from relative paths and running arbitrary system commands provided as input. These features make the skill susceptible to abuse, particularly if the commands to be executed are derived from untrusted repository content (indirect prompt injection).
- Socketwarn
1 alert: gptAnomaly
- Snykpass
Risk: LOW · No issues
- ZeroLeakspass
Score: 93/100 · 2 sections analyzed
What does this agent skill do?
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.
How can the creator link this skill?
Add the canonical catalog link to the repository README so users can inspect current installs and available audits. The publishing guide covers the complete discovery path.
<a href="https://skillzs.dev/skills/lllllllama/ai-paper-reproduction-skill/minimal-run-and-audit">View minimal-run-and-audit on skillZs</a>