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Minimal Run And Audit

  • 140k installs
  • 512 repo stars
  • Updated July 26, 2026
  • lllllllama/ai-paper-reproduction-skill

Minimal-run-and-audit is a rigor skill that captures standardized execution evidence from paper reproductions.

About

Minimal-run-and-audit is a rigor skill that executes documented smoke tests and evaluation commands, then captures evidence in standardized formats. It normalizes repro_outputs/ files, documents scientific changes via SCIENTIFIC_CHANGELOG, and provides a COMPARABILITY_REPORT. Use when running reproduction commands and needing auditable, comparable evidence.

  • Executes smoke tests and documented inference/evaluation commands with evidence capture
  • Normalizes repro_outputs/ files and generates SCIENTIFIC_CHANGELOG for methodology changes
  • Provides standardized COMPARABILITY_REPORT for paper-baseline alignment

Minimal Run And Audit by the numbers

  • 139,885 all-time installs (skills.sh)
  • +30 installs in the week ending Jul 28, 2026 (Skillselion tracking)
  • Ranked #7 of 2,066 Data Science & ML skills by installs in the Skillselion catalog
  • Security screen: MEDIUM risk (skills.sh audit)
  • Data as of Jul 28, 2026 (Skillselion catalog sync)
At a glance

minimal-run-and-audit capabilities & compatibility

Capabilities
command execution · evidence normalization · conflict documentation
npx skills add https://github.com/lllllllama/ai-paper-reproduction-skill --skill minimal-run-and-audit

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Listed on Skillselion
Installs140k
repo stars512
Security audit1 / 3 scanners passed
Last updatedJuly 26, 2026
Repositorylllllllama/ai-paper-reproduction-skill

What it does

ML researchers capture auditable execution evidence for paper reproductions with standardized reporting.

Who is it for?

ML researchers needing auditable, comparable reproduction evidence

Skip if: Developers still scoping the repository, bootstrapping conda environments, running full training jobs, or needing primary paper detail lookup.

When should I use this skill?

Running documented smoke tests or evaluation commands and need normalized output reporting

What you get

Normalized repro_outputs/ files, SCIENTIFIC_CHANGELOG, and COMPARABILITY_REPORT for baseline alignment

  • repro_outputs/ evidence files
  • execution patch notes

Files

SKILL.mdMarkdownGitHub ↗

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.

Related skills

Forks & variants (3)

Minimal Run And Audit has 3 known copies in the catalog totaling 176k installs. They canonicalize to this original listing.

FAQ

What does minimal-run-and-audit write after execution?

minimal-run-and-audit writes standardized repro_outputs/ files containing normalized execution evidence from the smoke test or inference command, plus patch notes when repository files were changed to enable the run.

What should minimal-run-and-audit not be used for?

minimal-run-and-audit should not be used for training execution, initial repo intake, environment setup, paper lookup, or end-to-end orchestration alone. It captures evidence from a selected minimal run only.

Is Minimal Run And Audit safe to install?

skills.sh reports 1 of 3 security scanners passed. Review the Security Audits panel on this page before installing in production.

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