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 reprooutputs/ 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.
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/lllllllama/rigorpilot-skills --skill minimal-run-and-audit- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads minimal-run-and-audit automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
Security audits
Independent scans of the skill's instructions and bundled files.
SKILL.md
The instructions your agent reads when the skill is triggered.
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.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, ../ai-research-reproduction/references/research-rigor-principles.md, scripts/run_command.py, and scripts/write_outputs.py.
FAQ
What does the minimal-run-and-audit skill do?
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 reprooutputs/ 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 minimal-run-and-audit?
Run npx skills add https://github.com/lllllllama/rigorpilot-skills --skill minimal-run-and-audit in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/minimal-run-and-audit/ (personal) or .claude/skills/minimal-run-and-audit/ (project).
Which AI agents can use minimal-run-and-audit?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is minimal-run-and-audit safe to install?
Security partners have reviewed it: Gen Agent Trust Hub passed; Socket flagged items to review; Snyk passed. As with any skill, read the SKILL.md and bundled scripts before installing.
Who maintains minimal-run-and-audit?
It is published by lllllllama in lllllllama/rigorpilot-skills. It has 477,420 recorded installs.
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Rigor Reproduce compatible skill slug for README-first deep learning repository reproduction. Use when the user wants an end-to-end, minimal-trustworthy flow that reads the repository first, selects the smallest documented inference or evaluation target, coordinates intake, setup, trusted execution, optional trusted training, optional repository analysis, and optional paper-gap resolution, enforces conservative patch rules, records evidence assumptions deviations and human decision points, and writes the standardized reprooutputs/ bundle. Do not use for paper summary, generic environment setup, isolated repo scanning, standalone command execution, silent protocol changes, score chasing, or broad research assistance outside repository-grounded reproduction.
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