minimal-run-and-audit
Trusted-lane execution and reporting skill for README-first AI 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, or end-to-end orchestration by itself.
Best use case
minimal-run-and-audit is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Trusted-lane execution and reporting skill for README-first AI 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, or end-to-end orchestration by itself.
Teams using minimal-run-and-audit should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/minimal-run-and-audit/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How minimal-run-and-audit Compares
| Feature / Agent | minimal-run-and-audit | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Trusted-lane execution and reporting skill for README-first AI 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, or end-to-end orchestration by itself.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# minimal-run-and-audit ## 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. ## 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 - clear distinction between verified, partial, and blocked states - `PATCHES.md` when repo files changed ## Notes Use `references/reporting-policy.md`, `scripts/run_command.py`, and `scripts/write_outputs.py`.
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