Agent skill

Minimal Run And Audit

by lllllllama in lllllllama/RigorPilot-Skills

Rigor Run skill for README-first deep learning repo reproduction.

MITAuto-check passedTesting & QA

Install Minimal Run And Audit

skills CLI
$ npx skills add lllllllama/RigorPilot-Skills --skill minimal-run-and-audit -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install lllllllama/RigorPilot-Skills minimal-run-and-audit --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/lllllllama/RigorPilot-Skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/minimal-run-and-audit .claude/skills/minimal-run-and-audit && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
minimal-run-and-audit
GitHub stars
497
Used in
1 other repo
Token cost
~691 tokens
SKILL.md length
271 words
Files
5 (incl. scripts, references)
Skills in repo
11
Repo updated
First seen
Licence
MIT

At a glance

Rigor Run skill for README-first deep learning repo reproduction.

  • The task is specifically to capture
  • SKILL.md covers When to apply, When not to apply, Clear boundaries and Input expectations, plus 2 more sections
  • Runs Python scripts from its folder
  • Normalize evidence from the selected smoke test

What it does

Minimal Run And Audit is an agent skill from lllllllama/RigorPilot-Skills. 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.

Its SKILL.md is about 690 tokens, which your agent loads only when the skill is triggered. The skill folder holds 7 other files, including scripts and reference files (for example `agents/openai.yaml`, `references/reporting-policy.md` and `scripts/run_command.py`).

It sits in Testing & QA, covering Deep learning, QA and bug reports and Academic paper search. The repository describes itself as: README-first research reproduction skills with bounded execution, auditable evidence, and byte-preserving README annotations. The licence is MIT.

When your agent uses it

  • The task is specifically to capture
  • Normalize evidence from the selected smoke test
  • Documented inference
  • Evaluation command and write standardized reprooutputs/ files

Example prompts

  • “/minimal-run-and-audit”

Requirements

  • Python 3

What it can do on your machine

Read from SKILL.md and the folder at commit fb3ccdf. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 2 files in scripts/ (Python), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Minimal Run And Audit loads about 691 tokens when it runs, and up to ~878 if it reads all its reference files. Until then it costs about 130 tokens; SKILL.md has 271 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~130
When it runs · the whole SKILL.md, loaded when a task matches
~691
With references · SKILL.md plus every file in references/, read only if the agent opens them
~878

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from lllllllama/RigorPilot-Skills at commit fb3ccdf, republished under its MIT licence (© lllllllama). 271 words, ~691 tokens.

Download SKILL.mdSave it as .claude/skills/minimal-run-and-audit/SKILL.md (or your agent's skills folder). This skill also uses 4 other files; get the full folder from GitHub.
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 ../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.

© lllllllama, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 4 other files (scripts, references) in skills/minimal-run-and-audit of lllllllama/RigorPilot-Skills.

  • SKILL.md
  • agents/openai.yaml
  • references/reporting-policy.md
  • scripts/run_command.py
  • scripts/write_outputs.py

Open the folder on GitHubat commit fb3ccdf

Used in 1 other repository

We found 1 copy of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in lllllllama/RigorPilot-Skills, which our catalogue first saw on October 7, 2026.

Compare with similar skills

Minimal Run And Audit next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

Minimal Run And Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Minimal Run And Audit this skilllllllllama/RigorPilot-Skills4971 repos~691Automated safety check: PassMIT
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Scrub Issuepytorch/pytorch104k—~4.6kAutomated safety check: PassCustom licence
Blackwell Build Compatibility Auditormirage-project/mirage2.5k—~1.7kAutomated safety check: PassApache-2.0
Edge Bringupexeex/edge-cores110—~1.7kAutomated safety check: NotesApache-2.0
Developer Experience Auditgarrytan/gstack136k—~19kAutomated safety check: NotesMIT

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Categories

Questions about Minimal Run And Audit

What does Minimal Run And Audit do?

Rigor Run skill for README-first deep learning repo reproduction. Minimal Run And Audit is an agent skill from lllllllama/RigorPilot-Skills. Rigor Run skill for README-first deep learning repo reproduction.

When should I use Minimal Run And Audit?

Minimal Run And Audit fits situations like: the task is specifically to capture; normalize evidence from the selected smoke test; documented inference; evaluation command and write standardized reprooutputs/ files.

How do I install Minimal Run And Audit in Claude Code?

Run `npx skills add lllllllama/RigorPilot-Skills --skill minimal-run-and-audit -a claude-code`. Or copy the skill folder (skills/minimal-run-and-audit in lllllllama/RigorPilot-Skills) into .claude/skills/minimal-run-and-audit in your project. Claude Code loads it when a task matches its description.

How do I install Minimal Run And Audit in Codex?

Run `npx skills add lllllllama/RigorPilot-Skills --skill minimal-run-and-audit -a codex`. Or copy the skill folder (skills/minimal-run-and-audit in lllllllama/RigorPilot-Skills) into .agents/skills/minimal-run-and-audit in your project. Codex loads it when a task matches its description.

Can I use Minimal Run And Audit in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add lllllllama/RigorPilot-Skills --skill minimal-run-and-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/minimal-run-and-audit, .gemini/skills/minimal-run-and-audit, .github/skills/minimal-run-and-audit and .opencode/skills/minimal-run-and-audit in your project.

What does Minimal Run And Audit need to run?

Going by SKILL.md and its folder, Minimal Run And Audit needs Python for the scripts in its folder. Our summary lists: Python 3.

Does Minimal Run And Audit access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Minimal Run And Audit safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does Minimal Run And Audit use?

Minimal Run And Audit is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Minimal Run And Audit use?

About 691 tokens (SKILL.md is roughly 2.8k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 187 tokens, read only when the agent opens those files.

What are the alternatives to Minimal Run And Audit?

Skills that share tags, products or a category with Minimal Run And Audit: Early Experience Data (OSU-NLP-Group/EarlyExperience, 103 stars), Scrub Issue (pytorch/pytorch, 104k stars), Blackwell Build Compatibility Auditor (mirage-project/mirage, 2.5k stars) and Edge Bringup (exeex/edge-cores, 110 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Minimal Run And Audit?

lllllllama (a GitHub user) maintains it in lllllllama/RigorPilot-Skills, which has 497 GitHub stars. The repository holds 11 skills in this directory. The repository was last updated on September 23, 2026.

Source: lllllllama/RigorPilot-Skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.