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.
Install with the open skills CLI (global, non-interactive — available in every Claude Code session):
npx skills add lllllllama/rigorpilot-skills --skill "minimal-run-and-audit" -g -a claude-code -yOr manually — clone and copy the skill directory (SKILL.md + companion files):
git clone --depth 1 https://github.com/lllllllama/rigorpilot-skills /tmp/rigorpilot-skills && cp -r /tmp/rigorpilot-skills/skills/minimal-run-and-audit ~/.claude/skills/minimal-run-and-audit-lllllllamaThis skill is a directory: SKILL.md is the entry point; the files below ship with it.
---
name: minimal-run-and-audit
description: 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
`../../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`, `../../references/research-rigor-principles.md`, `scripts/run_command.py`, and `scripts/write_outputs.py`.
Use when facing 2+ independent tasks that can be worked on without shared state or sequential dependencies
Use when encountering any bug, test failure, or unexpected behavior, before proposing fixes
Use when implementing any feature or bugfix, before writing implementation code