Download for MacApple silicon, macOS 14+
Skill

minimal-run-and-audit

@lllllllama 496k installs Trending #186 on Skills.Sh Updated 2026-09-23

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.

agent-skillsai-agentsdeep-learningreproducibilityresearch-tools

Install

With your agent

Install OMGSkills skill "lllllllama/rigorpilot-skills:minimal-run-and-audit" into this agent's normal skills directory: call the OMGSkills MCP get_install_plan tool for your agent and run the script it returns (it installs only the pinned commit and verifies SKILL.md), and stop if MCP or the pin is unavailable. Do not overwrite or change existing skills without asking me.

Manual command

git clone https://github.com/lllllllama/RigorPilot-Skills /tmp/RigorPilot-Skills && ln -s /tmp/RigorPilot-Skills/skills/minimal-run-and-audit ~/.claude/skills/minimal-run-and-audit

From README

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 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.