How do I install the Minimal Run And Audit skill?

Run npx skillmds@latest add lllllllama/minimal-run-and-audit in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Minimal Run And Audit safe to use?

This skill has not completed SkillMD's automated safety review yet. Capability flags: executes scripts. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Minimal Run And Audit?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Minimal Run And Audit free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Minimal Run And Audit?

lllllllama (@lllllllama) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Minimal Run And Audit by lllllllama

Name: Minimal Run And Audit
Rating: 4.5 (4 reviews)
Author: lllllllama

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.

lllllllama Updated Sep 9, 2026

File contents

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.

---name: minimal-run-and-auditdescription: 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.--- # 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`.

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